From c3b5f24e61c24853e6b9bb8a14959bf42fd7ae8f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 06:55:33 +0200 Subject: [PATCH 1/8] llamacpp: GPU offload, sequence-copy fan-out, marginal readout The llama.cpp backend was CPU-only and branched from the prefilled state by serializing and restoring sequence 0 once per decision. This keeps that path as the default and adds three things. --llama-gpu-layers N passes the offload count to llama.cpp. The metadata records it together with llama_supports_gpu_offload(), so a CPU wheel that silently ignores the flag is visible in the output. --llama-parallel N sets n_seq_max. Shared mode then copies the prefilled sequence 0 into up to N-1 branches with llama_memory_seq_cp and decodes all their suffixes in one batched llama_decode, reading one logit row per branch. Branches are removed whole afterwards, so sequence 0 needs no restore between chunks. The old docstring said hybrid Qwen3.5 memories support neither sequence copies nor partial tail removal; only the second half is true. seq_cp is implemented for llama_memory_recurrent, and both branching modes rely on it. What hybrid memories refuse is truncating a tail back to the prefix, because a recurrent state cannot be partially erased. The docstring and docs now say so. --llama-readout marginal (shared mode, parallel >= 2) folds one-token preambles into the answer-slot masses: for each branch whose most likely next tokens are not slots, the probes are appended in one extra batched decode and the slot masses read after them are added, weighted by the preamble's probability. option_logits become log-masses so softmax and temperature scaling are unchanged; preamble_mass records the share that came through a preamble. Qwen3-8B, which wants to write "**" before the letter, is the case this exists for; on Qwen3.5-4B it changes nothing. benchmarks/shape777.py accepts --backend llamacpp with the same options, so the 37x21 systems fixture runs on GGUF, and reads the GPU name without a CUDA torch. Unit tests cover the new validation, the fan-out batch layout, branch removal, and the marginal fold; the real-GGUF test now also loads with three sequences and compares copy-shared to restore-shared. It passes on the PyPI CPU wheel and on the cu124 wheel with full offload. --- README.md | 19 +- benchmarks/shape777.py | 57 ++++- docs/LLAMACPP.md | 151 +++++++++++++ src/semif_phase1/cli.py | 23 +- src/semif_phase1/llamacpp_backend.py | 319 +++++++++++++++++++++++---- tests/test_llamacpp.py | 170 +++++++++++++- 6 files changed, 669 insertions(+), 70 deletions(-) create mode 100644 docs/LLAMACPP.md diff --git a/README.md b/README.md index 290666f..72b7778 100644 --- a/README.md +++ b/README.md @@ -54,17 +54,14 @@ export HF_HOME=/path/to/large-drive/huggingface pip install -e '.[test]' ``` -**CPU only:** the llama.cpp backend scores the same prompts from a local GGUF -checkpoint with no CUDA device. Install `pip install -e '.[test,llamacpp]'`, -fetch a GGUF (for example `Qwen_Qwen3.5-4B-Q4_K_M.gguf` from -`bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf -/path/to/model.gguf`; `--llama-threads` caps the CPU threads. Prompt -construction stays on the pinned reference tokenizer, so `prompt_sha256` -matches the Torch backend row for row; scores carry the GGUF checksum and are -conditional on the quantized weights. Direct and prefix-cached execution can -have small numerical differences from different llama.cpp evaluation paths; -compare decisions or probabilities with a tolerance rather than raw logits -bit for bit. One loaded backend owns one stateful scoring context. +**llama.cpp / GGUF:** the llama.cpp backend scores the same prompts from a local GGUF +checkpoint, on CPU or with `--llama-gpu-layers` offloaded to a GPU. Install +`pip install -e '.[test,llamacpp]'`, download a GGUF of the pinned model (for example +`bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf /path/to/model.gguf`. +Shared mode can fan its questions out over copied sequences in one batched decode with +`--llama-parallel N`, which also works on hybrid Qwen3.5 memories. Prompt hashes match the +Torch backend; quantized option scores have small numerical differences. See +[docs/LLAMACPP.md](docs/LLAMACPP.md). Run the owned examples: diff --git a/benchmarks/shape777.py b/benchmarks/shape777.py index feeb301..cb9dd0e 100644 --- a/benchmarks/shape777.py +++ b/benchmarks/shape777.py @@ -11,9 +11,18 @@ from pathlib import Path from semif_phase1.core import load_causal_model -from semif_phase1.direct import score -from semif_phase1.serial import SerialPrefixScorer -from semif_phase1.shared import score_shared +from semif_phase1.direct import score as torch_score +from semif_phase1.serial import SerialPrefixScorer as TorchSerialPrefixScorer +from semif_phase1.shared import score_shared as torch_score_shared + + +def _gpu_name() -> str | None: + """The NVIDIA device name without importing a CUDA-enabled torch.""" + for info in sorted(Path("/proc/driver/nvidia/gpus").glob("*/information")): + for line in info.read_text().splitlines(): + if line.startswith("Model:"): + return line.split(":", 1)[1].strip() + return None def main() -> None: @@ -23,17 +32,47 @@ def main() -> None: parser.add_argument("--input", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--max-tokens", type=int, default=4096) + parser.add_argument("--backend", choices=("torch", "llamacpp"), default="torch") + parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") + parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp") + parser.add_argument("--llama-gpu-layers", type=int, default=0, + help="Layers offloaded to the GPU for --backend llamacpp (default: 0)") + parser.add_argument("--llama-parallel", type=int, default=1, + help="Branch slots for llama.cpp shared mode (default: 1 = state restore)") args = parser.parse_args() if args.output.exists(): parser.error("Output must be new") + if args.backend == "llamacpp": + if args.gguf is None or not args.gguf.is_file(): + parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") + if args.llama_gpu_layers < 0 or args.llama_parallel < 1: + parser.error("--llama-gpu-layers must be nonnegative and --llama-parallel positive") + elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers or args.llama_parallel != 1: + parser.error("llama.cpp options require --backend llamacpp") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] groups = defaultdict(list) for row in rows: groups[row["group_id"]].append(row) if len(rows) != 777 or len(groups) != 37 or any(len(group) != 21 for group in groups.values()): parser.error("Expected the committed 37-state x 21-question fixture") - model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") - import torch + if args.backend == "llamacpp": + from semif_phase1 import llamacpp_backend + + model, tokenizer, metadata = llamacpp_backend.load_model( + args.model, args.revision, args.gguf, threads=args.llama_threads, + context_tokens=args.max_tokens, gpu_layers=args.llama_gpu_layers, + sequences=args.llama_parallel) + score = llamacpp_backend.score + SerialPrefixScorer = llamacpp_backend.SerialPrefixScorer + score_shared = llamacpp_backend.score_shared + cuda = None + hardware = (_gpu_name() or "unknown GPU") if args.llama_gpu_layers else "CPU" + else: + model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") + import torch as cuda + + score, SerialPrefixScorer, score_shared = torch_score, TorchSerialPrefixScorer, torch_score_shared + hardware = cuda.cuda.get_device_name(0) first = next(iter(groups.values())) score(model, tokenizer, first[0], metadata, args.max_tokens) @@ -45,13 +84,15 @@ def main() -> None: "version": "shape777-published-v1", "input_sha256": hashlib.sha256(args.input.read_bytes()).hexdigest(), "model": metadata, - "hardware": torch.cuda.get_device_name(0), + "hardware": hardware, + "backend": args.backend, "timing_scope": "Warm model; includes prompt construction, tokenization, transfers, forward passes and CPU readout.", "results": [], } predictions = {} for mode in ("fresh", "serial_prefix", "parallel_shared"): - torch.cuda.reset_peak_memory_stats() + if cuda is not None: + cuda.cuda.reset_peak_memory_stats() started = time.perf_counter() values, state_times = [], [] for group in groups.values(): @@ -73,7 +114,7 @@ def main() -> None: "wall_seconds": elapsed, "decisions_per_second": len(values) / elapsed, "state_p50_seconds": statistics.median(state_times), - "peak_cuda_bytes": torch.cuda.max_memory_allocated(), + "peak_cuda_bytes": cuda.cuda.max_memory_allocated() if cuda is not None else None, } ) reference = {row["id"]: row for row in predictions["fresh"]} diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md new file mode 100644 index 0000000..51aa377 --- /dev/null +++ b/docs/LLAMACPP.md @@ -0,0 +1,151 @@ +# llama.cpp / GGUF + +The llama.cpp backend runs SemIf's direct, serial-prefix, and shared decision +modes over a quantized GGUF checkpoint, on CPU or with layers offloaded to a +GPU. Prompts, answer-slot checks and `prompt_sha256` come from the reference +transformers tokenizer, exactly as in the Torch backend; llama.cpp only executes +the forward pass. Every prompt is re-tokenized through the GGUF vocabulary and +must match the reference encoding before it is scored. No answer token is +generated. + +## Install and score + +```bash +python3 -m venv .venv +source .venv/bin/activate +pip install -e '.[test,llamacpp]' # CPU wheel of llama-cpp-python + +semif-score --backend llamacpp --mode direct \ + --model Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input examples/decisions.jsonl \ + --output results-llamacpp-direct.jsonl +``` + +The pinned tokenizer is downloaded into the Hugging Face cache on first use +(a few megabytes; the model weights come from the GGUF file). The GGUF used for +the results below is `bartowski/Qwen_Qwen3.5-4B-GGUF` at revision +`4168f45a16a1290d65a4ec0fa312ae917a4c15d6`, quantization Q4_K_M, 3 013 027 808 +bytes, SHA-256 `13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983`. +Each prediction records the file's size and checksum. + +### GPU offload + +`--llama-gpu-layers N` offloads `N` layers (use a large value such as `99` for +all of them). It needs a llama-cpp-python wheel built with a GPU backend; the +PyPI wheel is CPU-only. For CUDA: + +```bash +pip install --force-reinstall --no-deps "llama-cpp-python==0.3.35" \ + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124 +pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12 nvidia-cuda-nvrtc-cu12 +export LD_LIBRARY_PATH=$(ls -d .venv/lib/python3.*/site-packages/nvidia/*/lib | tr '\n' ':') +``` + +The metadata records `n_gpu_layers` and whether the loaded library supports +offload at all, so a CPU wheel silently ignoring the flag is visible in the +output. + +## Two ways to branch from one state + +Sequence 0 of the llama.cpp context holds the prefilled state. Shared mode +scores every question of that state from a branch of it, and there are two +ways to make a branch: + +| `--llama-parallel` | branching | decodes per state | +|---|---|---| +| `1` (default) | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | +| `N ≥ 2` | **sequence copy** — `llama_memory_seq_cp` of sequence 0 into up to `N − 1` throwaway sequences, all suffixes in one batched `llama_decode` | one per `N − 1` questions | + +State restore is what the backend has always done. Sequence copy is the +llama.cpp equivalent of the Torch backend's `native-state-prefix-parallel-v1`: +the prefix is evaluated once, copied without serialization, and the branch +suffixes share a single decode. The branches are removed whole afterwards +(whole-sequence removal never fails), so sequence 0 is left exactly as +prefilled and the next chunk of questions needs no restore at all. + +### Hybrid models + +Qwen3.5 is a hybrid architecture: one full-attention layer in four, the rest +Gated DeltaNet with a recurrent state. In llama.cpp that state lives in +`llama_memory_recurrent`, which keeps only the state after the last token and +therefore **cannot be partially erased** — `llama_memory_seq_rm` refuses any +range that includes a sequence's last position (the source says: "models like +Mamba or RWKV can't have a state partially erased at the end of the sequence"). +Truncating a branch back to the prefix is thus impossible on these models, and +that is a property of the architecture, not of a build or a binding. + +What hybrid memories do support is whole-sequence removal, state save/restore, +and **sequence copies** — `llama_memory_recurrent::seq_cp` is implemented — which +is why both branching modes above work on Qwen3.5. Pure-attention models +additionally allow tail truncation; the backend does not rely on it. + +`n_seq_max` is a context parameter (`llama_context_params.n_seq_max`), not a +build option; the same wheel serves both modes. The context is allocated as +`n_seq_max × (max_tokens + 64)` tokens and, when the library exposes it, +`kv_unified` is enabled so the copied branches share the prefix's attention +cells. + +## Validation + +`tests/test_llamacpp.py::test_real_gguf_scores_direct_serial_and_shared` loads the +real GGUF twice — with one sequence and with three — and checks that direct, +serial, restore-shared, copy-shared and marginal readouts agree on the +decisions, that copy-shared option logits match restore-shared ones within +0.5, and that `preamble_mass` stays in `[0, 1]`. Set `SEMIF_LLAMACPP_GGUF` to run +it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: + +- the PyPI CPU wheel of `llama-cpp-python` 0.3.35, CPU only; +- the cu124 wheel of the same version with every layer offloaded to an RTX 3080 + Laptop GPU. + +**Known issue, not in this backend:** the cu124 wheel's *CPU* code path raises +`Illegal instruction` on an Intel Xeon W-11955M (AVX-512 without `avx512_bf16` +or AMX) for any decode, single- or multi-sequence, hybrid or pure-attention +model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. + +## Marginal readout + +`--llama-readout marginal` (shared mode, `--llama-parallel ≥ 2`) changes how +the answer is read, not what is asked. Some checkpoints want to emit a token +*before* the letter — Qwen3-8B puts almost all of its next-token mass on the +Markdown `**` token when the assistant turn starts empty, and reading only the +last prompt position then reads that preamble instead of the answer. The +marginal readout takes, for each branch, the most likely non-slot tokens above +a floor (two probes, 5 %), appends each in one extra batched decode across all +branches, reads the slot masses after it, and adds them to the direct slot +masses weighted by the preamble's probability. `option_logits` then hold the +natural logs of the summed masses, so `softmax` and temperature scaling keep +working unchanged; `preamble_mass` records the share that came through a +preamble. On Qwen3.5-4B, which answers with the letter directly, the readout +changes nothing and `preamble_mass` stays at zero. + +## Results + + + +## Reproduce + +```bash +# Quality: the owned labeled workload, three serving configurations +for MODE in direct serial shared; do + semif-score --backend llamacpp --mode $MODE --llama-gpu-layers 99 --llama-parallel 8 \ + --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/authored144.jsonl \ + --output results/raw/predictions/llamacpp-gguf-cuda-$MODE-authored144.jsonl +done +python benchmarks/evaluate.py --gold benchmarks/data/authored144.jsonl \ + --predictions results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl + +# Systems: the 37x21 fixture, fresh / serial-prefix / parallel-shared +python benchmarks/shape777.py --backend llamacpp --llama-gpu-layers 99 --llama-parallel 8 \ + --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/shape777.jsonl \ + --output results/raw/shape777-llamacpp-gguf-cuda.json +``` + +Shared mode requires every row of the input to carry the same exact state; +`shape777.py` groups its fixture by `group_id` and does that for you. diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index dd2c2f3..2b99936 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -23,6 +23,14 @@ def main() -> None: parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp (default: all visible cores)") + parser.add_argument("--llama-gpu-layers", type=int, default=0, + help="Layers to offload to the GPU for --backend llamacpp (default: 0, CPU only)") + parser.add_argument("--llama-parallel", type=int, default=1, + help="Branch slots for --backend llamacpp shared mode: suffixes are fanned out over " + "copied sequences and decoded together (default: 1 = state restore per decision)") + parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", + help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " + "over one-token preambles (needs --llama-parallel > 1)") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -51,11 +59,22 @@ def main() -> None: parser.error("--llama-threads must be positive") if args.backend == "mlx" and args.mode == "reranker": parser.error("MLX supports direct, serial, and shared modes; reranker requires torch") + for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): + if args.backend != "llamacpp" and value != parser.get_default(name.lstrip("-").replace("-", "_")): + parser.error(f"{name} requires --backend llamacpp") + if args.llama_readout != "last" and args.backend != "llamacpp": + parser.error("--llama-readout requires --backend llamacpp") if args.backend == "llamacpp": if args.mode == "reranker": parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") + if args.llama_gpu_layers < 0: + parser.error("--llama-gpu-layers must be nonnegative") + if args.llama_parallel < 1: + parser.error("--llama-parallel must be positive") + if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel < 2): + parser.error("--llama-readout marginal requires --mode shared and --llama-parallel of at least 2") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") @@ -75,7 +94,9 @@ def main() -> None: model, tokenizer, metadata = llamacpp_backend.load_model( args.model, args.revision, args.gguf, - threads=args.llama_threads, context_tokens=args.max_tokens) + threads=args.llama_threads, context_tokens=args.max_tokens, + gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel, + readout=args.llama_readout) direct, serial, shared = (llamacpp_backend.score, llamacpp_backend.SerialPrefixScorer, llamacpp_backend.score_shared) else: diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 5600ae8..446e517 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -1,4 +1,4 @@ -"""CPU option readout over GGUF checkpoints through llama.cpp. +"""Option readout over GGUF checkpoints through llama.cpp, on CPU or with GPU offload. Prompt construction and answer-slot verification stay on the reference transformers tokenizer, so prompt_sha256 matches the Torch backend exactly; @@ -6,11 +6,21 @@ Every scored prompt is re-tokenized through the GGUF vocabulary and must agree with the reference encoding before it is evaluated. -Sequence 0 of the single context holds the prefill; each decision restores the -saved prefix state (whole-sequence save/restore) before decoding its suffix. -The hybrid linear-attention memory of Qwen3.5 supports neither sequence -copies nor partial tail removal, so branch replication goes through the -per-sequence state serialization llama.cpp itself uses for slot caching. +Sequence 0 of the context holds the state prefill. Two ways to branch from it: + +* **state restore** (``sequences == 1``): each decision restores the saved + sequence-0 state, then decodes its suffix. One restore per decision. +* **sequence copy** (``sequences > 1``): the prefix is copied with + ``llama_memory_seq_cp`` into up to ``sequences - 1`` throwaway sequences and + all their suffixes are decoded in one batched ``llama_decode``. No state + serialization, no restore; the branches are removed whole afterwards. + +Hybrid linear-attention models such as Qwen3.5 keep a recurrent state that +cannot be partially erased, so ``llama_memory_seq_rm`` refuses to truncate a +tail back to the prefix. They do support whole-sequence removal, state +save/restore, and sequence copies (``llama_memory_recurrent::seq_cp``), which is +what both branching modes rely on. Pure-attention models additionally allow +tail truncation, which the optional marginal readout uses when available. """ from __future__ import annotations @@ -31,17 +41,18 @@ from .shared import _state_prefix DECODE_CHUNK = 512 +READOUTS = ("last", "marginal") _BACKEND_INITIALIZED = False -def _cpu_model_params(library): - """Initialize llama.cpp once and return model parameters with GPU offload disabled.""" +def _model_params(library, gpu_layers: int): + """Initialize llama.cpp once and return model parameters with the requested GPU offload.""" global _BACKEND_INITIALIZED if not _BACKEND_INITIALIZED: library.llama_backend_init() _BACKEND_INITIALIZED = True params = library.llama_model_default_params() - params.n_gpu_layers = 0 + params.n_gpu_layers = gpu_layers return params @@ -76,19 +87,38 @@ def _logsumexp(values: numpy.ndarray) -> float: return peak + float(numpy.log(numpy.exp(values - peak).sum())) +def _stable_softmax(values: numpy.ndarray) -> numpy.ndarray: + shifted = values - values.max() + weights = numpy.exp(shifted) + return weights / weights.sum() + + class _Engine: """One llama.cpp context bound to a loaded GGUF model.""" - def __init__(self, library, model, context_tokens: int, threads: int): + def __init__(self, library, model, context_tokens: int, threads: int, sequences: int = 1): + if not (isinstance(sequences, int) and sequences >= 1): + raise ValueError("sequences must be a positive integer") params = library.llama_context_default_params() - params.n_ctx = context_tokens - params.n_seq_max = 1 - # llama.cpp requires n_outputs_max >= n_seq_max * n_outputs_max_per_seq. - params.n_outputs_max = 1 + # Sequence 0 keeps the prefix; the others are branch slots. Without a unified KV buffer + # llama.cpp splits n_ctx across sequences, so size it for the worst case either way. + params.n_ctx = context_tokens * sequences + params.n_seq_max = sequences + # One flagged output per sequence in a fan-out batch; llama.cpp requires + # n_outputs_max >= n_seq_max * n_outputs_max_per_seq. + params.n_outputs_max = sequences + params.n_outputs_max_per_seq = 1 params.n_threads = threads params.n_threads_batch = threads + unified = None + if sequences > 1 and hasattr(params, "kv_unified"): + # Branches share the whole prefix; llama.h recommends the unified buffer for that case. + params.kv_unified = True + unified = True self.lib = library self.model = model + self.sequences = sequences + self.kv_unified = unified self.context = library.llama_init_from_model(model, params) if not self.context: raise RuntimeError("llama.cpp failed to create the scoring context") @@ -99,6 +129,7 @@ def __init__(self, library, model, context_tokens: int, threads: int): raise RuntimeError("llama.cpp returned no context memory") self.context_tokens = int(library.llama_n_ctx(self.context)) self.vocab_size = library.llama_n_vocab(library.llama_model_get_vocab(model)) + self.can_truncate = None # learned on first attempt; depends on the model architecture def close(self) -> None: if self.context: @@ -106,6 +137,14 @@ def close(self) -> None: self.context = None self.memory = None + def _logits_at(self, index: int) -> numpy.ndarray: + pointer = self.lib.llama_get_logits_ith(self.context, index) + if not pointer: + raise RuntimeError("llama.cpp returned no logits for the flagged position") + return numpy.ctypeslib.as_array( + ctypes.cast(pointer, ctypes.POINTER(ctypes.c_float)), shape=(self.vocab_size,) + ).copy() + def _decode(self, tokens: list[int], start: int, sequence: int, want_logits: bool): if not tokens: raise ValueError("Refusing to decode an empty token list") @@ -127,12 +166,36 @@ def _decode(self, tokens: list[int], start: int, sequence: int, want_logits: boo self.lib.llama_batch_free(batch) if not want_logits: return None - pointer = self.lib.llama_get_logits_ith(self.context, -1) - if not pointer: - raise RuntimeError("llama.cpp returned no logits for the flagged position") - return numpy.ctypeslib.as_array( - ctypes.cast(pointer, ctypes.POINTER(ctypes.c_float)), shape=(self.vocab_size,) - ).copy() + return self._logits_at(-1) + + def _decode_items(self, items: list[tuple[int, int, int, bool]]) -> dict[int, numpy.ndarray]: + """Decode (token, position, sequence, want_logits) items across sequences in one pass. + + Logits are read right after the chunk that produced them: llama.cpp only exposes the + outputs of the most recent ``llama_decode`` call. + """ + if not items: + raise ValueError("Refusing to decode an empty item list") + collected: dict[int, numpy.ndarray] = {} + for offset in range(0, len(items), DECODE_CHUNK): + chunk = items[offset : offset + DECODE_CHUNK] + batch = self.lib.llama_batch_init(len(chunk), 0, self.sequences) + try: + for index, (token, position, sequence, want) in enumerate(chunk): + batch.token[index] = token + batch.pos[index] = position + batch.n_seq_id[index] = 1 + batch.seq_id[index][0] = sequence + batch.logits[index] = int(want) + batch.n_tokens = len(chunk) + if self.lib.llama_decode(self.context, batch): + raise RuntimeError("llama_decode failed; raise --max-tokens if prompts grew") + for index, (_, _, sequence, want) in enumerate(chunk): + if want: + collected[sequence] = self._logits_at(index) + finally: + self.lib.llama_batch_free(batch) + return collected def clear(self) -> None: self.lib.llama_memory_clear(self.memory, False) @@ -164,6 +227,44 @@ def full_logits(self, tokens: list[int]) -> numpy.ndarray: self.clear() return self._decode(tokens, 0, 0, True) + # --- sequence-copy fan-out ------------------------------------------------------------- + + def fanout_logits(self, prefix_length: int, suffixes: list[list[int]]) -> list[numpy.ndarray]: + """Copy the sequence-0 prefix into one branch per suffix and decode them all at once. + + Returns one full-vocabulary logit vector per suffix, then removes the branches whole so + sequence 0 is left exactly as prefilled. Whole-sequence removal never fails, on any + memory type. + """ + if not suffixes or any(not suffix for suffix in suffixes): + raise ValueError("Every branch needs a nonempty suffix") + if len(suffixes) > self.sequences - 1: + raise ValueError(f"{len(suffixes)} branches exceed the {self.sequences - 1} configured slots") + branches = list(range(1, len(suffixes) + 1)) + for branch in branches: + self.lib.llama_memory_seq_cp(self.memory, 0, branch, -1, -1) + items = [ + (token, prefix_length + offset, branch, offset == len(suffix) - 1) + for branch, suffix in zip(branches, suffixes) + for offset, token in enumerate(suffix) + ] + try: + logits = self._decode_items(items) + return [logits[branch] for branch in branches] + finally: + for branch in branches: + self.lib.llama_memory_seq_rm(self.memory, branch, -1, -1) + + def probe_branches(self, positions: dict[int, int], tokens: dict[int, int]) -> dict[int, numpy.ndarray]: + """Append one token to each listed branch and read the logits after it. + + Used by the marginal readout: ``tokens[branch]`` is the preamble token the model wanted + to emit first, ``positions[branch]`` the position it goes to. Branches are live sequences + from the current fan-out; the caller removes them. + """ + items = [(tokens[branch], positions[branch], branch, True) for branch in sorted(tokens)] + return self._decode_items(items) + def _free_native(engine: _Engine, library, model) -> None: engine.close() @@ -208,8 +309,14 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, - context_tokens: int = 4096): - """Load one pinned reference tokenizer plus a local GGUF checkpoint for CPU scoring.""" + context_tokens: int = 4096, gpu_layers: int = 0, sequences: int = 1, + readout: str = "last"): + """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. + + ``gpu_layers`` is passed straight to llama.cpp (0 keeps the historical CPU behaviour, a + large value offloads every layer). ``sequences`` is the context's ``n_seq_max``: with more + than one, shared scoring fans suffixes out over copied sequences instead of restoring state. + """ local = Path(source).is_dir() if not local and not re.fullmatch(r"[0-9a-f]{40}", revision or ""): raise ValueError("Remote sources require a pinned 40-character revision; local sources require a revision label") @@ -224,6 +331,12 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, threads = os.cpu_count() or 4 if not (isinstance(threads, int) and threads >= 1): raise ValueError("threads must be a positive integer") + if not (isinstance(gpu_layers, int) and gpu_layers >= 0): + raise ValueError("gpu_layers must be a nonnegative integer") + if not (isinstance(sequences, int) and sequences >= 1): + raise ValueError("sequences must be a positive integer") + if readout not in READOUTS: + raise ValueError(f"readout must be one of {READOUTS}") import transformers try: import llama_cpp @@ -241,13 +354,13 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, checksum.update(block) gguf_record = {"file": gguf.name, "bytes": gguf.stat().st_size, "sha256": checksum.hexdigest()} - model_params = _cpu_model_params(llama_cpp) + model_params = _model_params(llama_cpp, gpu_layers) model = llama_cpp.llama_model_load_from_file(str(gguf).encode("utf-8"), model_params) if not model: raise RuntimeError(f"llama.cpp failed to load the GGUF checkpoint: {gguf}") window = context_tokens + 64 try: - engine = _Engine(llama_cpp, model, window, threads) + engine = _Engine(llama_cpp, model, window, threads, sequences) vocab = llama_cpp.llama_model_get_vocab(model) _verify_vocabulary(tokenizer, llama_cpp, vocab) except Exception: @@ -263,7 +376,11 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "gguf": gguf_record, "vocab_size": engine.vocab_size, "threads": threads, - "n_gpu_layers": 0, + "n_gpu_layers": gpu_layers, + "gpu_offload_supported": bool(llama_cpp.llama_supports_gpu_offload()), + "n_seq_max": sequences, + "kv_unified": engine.kv_unified, + "readout_mode": readout, "max_prompt_tokens": context_tokens, "context_tokens": engine.context_tokens, "decode_chunk": DECODE_CHUNK, @@ -358,8 +475,66 @@ def score(self, row: dict) -> dict: return result +def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, + suffixes: list[list[int]], vocabularies: list[numpy.ndarray], + slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): + """Fold one-token preambles back into the answer-slot masses. + + Some checkpoints want to emit a token before the letter — Qwen3-8B puts almost all of its + mass on the Markdown ``**`` token when the assistant turn starts empty. Reading only the last + prompt position then reads the preamble, not the answer. For each branch whose most likely + next tokens are not answer slots, this appends up to ``probes`` such tokens (one extra batched + decode per probe rank across all branches), reads the slot masses after them, and adds them + to the direct slot masses weighted by the preamble's probability. The argmax and the reported + ``option_logits`` become log-masses of that truncated path sum. + + Returns per-branch (option_logits, preamble_mass) with option_logits as natural logs of the + summed masses, so downstream softmax and temperature scaling keep working unchanged. + """ + live = {branch: prefix_length + len(suffix) for branch, suffix in zip(branches, suffixes)} + masses, via = [], [] + for vocabulary, slots in zip(vocabularies, slot_lists): + probabilities = _stable_softmax(vocabulary.astype(numpy.float64)) + masses.append(probabilities[slots].copy()) + via.append(0.0) + # Candidate preambles per branch: top tokens that are not answer slots. + candidates: list[list[int]] = [] + for vocabulary, slots in zip(vocabularies, slot_lists): + forbidden = set(slots) + order = numpy.argsort(-vocabulary)[: probes + len(slots)] + candidates.append([int(token) for token in order if int(token) not in forbidden][:probes]) + weights = [_stable_softmax(vocabulary.astype(numpy.float64)) for vocabulary in vocabularies] + for rank in range(probes): + tokens, positions = {}, {} + for index, branch in enumerate(branches): + if rank < len(candidates[index]): + token = candidates[index][rank] + if weights[index][token] >= floor: + tokens[branch] = token + positions[branch] = live[branch] + if not tokens: + break + after = engine.probe_branches(positions, tokens) + for index, branch in enumerate(branches): + if branch in tokens: + weight = float(weights[index][tokens[branch]]) + gain = weight * _stable_softmax(after[branch].astype(numpy.float64))[slot_lists[index]] + masses[index] += gain + via[index] += float(gain.sum()) + live[branch] += 1 + results = [] + for mass, mass_via in zip(masses, via): + total = float(mass.sum()) + results.append((numpy.log(numpy.maximum(mass, 1e-300)).tolist(), mass_via / total if total > 0 else 0.0)) + return results + + def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): - """Prefill one exact state once, then score every criterion from restored branches.""" + """Prefill one exact state once, then score every criterion from branches. + + With a single sequence the branches are restored from a saved state, one at a time. With + ``n_seq_max > 1`` they are sequence copies decoded together, in chunks of ``n_seq_max - 1``. + """ if not rows or any(row["state"] != rows[0]["state"] for row in rows[1:]): raise ValueError("Shared scoring requires one nonempty exact state") if len({row["id"] for row in rows}) != len(rows): @@ -370,25 +545,83 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: if not prefix or any(ids[: len(prefix)] != prefix or len(ids) <= len(prefix) for ids, _, _ in encoded): raise ValueError("The fixed state prefix does not match every full prompt") encode_seconds = time.perf_counter() - started + engine = model.engine + marginal = metadata.get("readout_mode") == "marginal" mark = time.perf_counter() - model.engine.clear() - model.engine.prefill(prefix) - state_data = model.engine.save_state() + engine.clear() + engine.prefill(prefix) + if engine.sequences == 1: + state_data = engine.save_state() prefill_seconds = time.perf_counter() - mark copy_seconds = suffix_seconds = 0.0 results = [] - for row, row_encoded in zip(rows, encoded): - ids, slots, _ = row_encoded - mark = time.perf_counter() - model.engine.restore_state(state_data) - copy_seconds += time.perf_counter() - mark - mark = time.perf_counter() - vocabulary = model.engine.branch_logits(len(prefix), ids[len(prefix) :]) - suffix_seconds += time.perf_counter() - mark - results.append(_result( - row, row_encoded, vocabulary[slots].tolist(), vocabulary, metadata, - "llamacpp-state-restore-shared-v1", "quantized branch last-position logits over a restored prefix state", - )) + if engine.sequences == 1: + if marginal: + raise ValueError("The marginal readout needs sequence copies; load with sequences > 1") + for row, row_encoded in zip(rows, encoded): + ids, slots, _ = row_encoded + mark = time.perf_counter() + engine.restore_state(state_data) + copy_seconds += time.perf_counter() - mark + mark = time.perf_counter() + vocabulary = engine.branch_logits(len(prefix), ids[len(prefix) :]) + suffix_seconds += time.perf_counter() - mark + results.append(_result( + row, row_encoded, vocabulary[slots].tolist(), vocabulary, metadata, + "llamacpp-state-restore-shared-v1", + "quantized branch last-position logits over a restored prefix state", + )) + branch_bytes = state_data[1] + config = "llamacpp-state-restore-shared-v1" + else: + width = engine.sequences - 1 + for start in range(0, len(rows), width): + chunk_rows = rows[start : start + width] + chunk_encoded = encoded[start : start + width] + suffixes = [ids[len(prefix) :] for ids, _, _ in chunk_encoded] + slot_lists = [slots for _, slots, _ in chunk_encoded] + branches = list(range(1, len(suffixes) + 1)) + mark = time.perf_counter() + for branch in branches: + engine.lib.llama_memory_seq_cp(engine.memory, 0, branch, -1, -1) + copy_seconds += time.perf_counter() - mark + mark = time.perf_counter() + try: + items = [ + (token, len(prefix) + offset, branch, offset == len(suffix) - 1) + for branch, suffix in zip(branches, suffixes) + for offset, token in enumerate(suffix) + ] + logits = engine._decode_items(items) + vocabularies = [logits[branch] for branch in branches] + if marginal: + folded = _marginal_readout(engine, branches, len(prefix), suffixes, + vocabularies, slot_lists) + finally: + for branch in branches: + engine.lib.llama_memory_seq_rm(engine.memory, branch, -1, -1) + suffix_seconds += time.perf_counter() - mark + for index, (row, row_encoded) in enumerate(zip(chunk_rows, chunk_encoded)): + vocabulary = vocabularies[index] + if marginal: + selected, preamble_mass = folded[index] + result = _result( + row, row_encoded, selected, vocabulary, metadata, + "llamacpp-seq-copy-parallel-marginal-v1", + "quantized branch logits over copied prefix sequences, answer-slot masses " + "summed over one-token preambles; option_logits are log-masses", + ) + result["preamble_mass"] = preamble_mass + else: + result = _result( + row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, + "llamacpp-seq-copy-parallel-v1", + "quantized branch last-position logits over copied prefix sequences; " + "one batched decode per chunk", + ) + results.append(result) + branch_bytes = 0 + config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) timing = { "total_seconds": time.perf_counter() - started, @@ -398,7 +631,9 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "replicate_seconds": copy_seconds, "suffix_forward_seconds": suffix_seconds, "batch_size": len(rows), - "branch_state_bytes": state_data[1], + "branch_slots": engine.sequences - 1, + "branch_state_bytes": branch_bytes, + "serving_config": config, "true_suffix_tokens": suffix_total, "padded_suffix_tokens": suffix_total, } diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 29608cb..3a7a48d 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -16,6 +16,9 @@ (["--mode", "reranker", "--backend", "llamacpp", "--gguf", "x.gguf"], "reranker requires torch"), (["--mode", "direct", "--backend", "llamacpp"], "requires --gguf"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "missing.gguf"], "requires --gguf"), + (["--mode", "direct", "--llama-gpu-layers", "99"], "--llama-gpu-layers requires --backend llamacpp"), + (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), + (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): monkeypatch.setattr(sys, "argv", ["semif-score", "--model", "unused", "--revision", "unused", @@ -31,8 +34,9 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): import semif_phase1 fake_backend = SimpleNamespace( - load_model=lambda source, revision, gguf, *, threads, context_tokens: - (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens}), + load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences, readout: + (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens, + "gpu_layers": gpu_layers, "sequences": sequences, "readout": readout}), score=lambda model, tokenizer, row, metadata, max_tokens: metadata, SerialPrefixScorer=None, score_shared=None, ) @@ -50,6 +54,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 + assert record["gpu_layers"] == 0 and record["sequences"] == 1 and record["readout"] == "last" def test_load_model_rejects_unpinned_remote_revision(): @@ -76,7 +81,7 @@ def test_engine_refuses_empty_decode(): engine._decode([], 0, 0, False) -def test_cpu_model_params_initialize_once_and_disable_offload(monkeypatch): +def test_model_params_initialize_once_and_apply_offload(monkeypatch): from semif_phase1 import llamacpp_backend calls = [] @@ -85,10 +90,10 @@ def test_cpu_model_params_initialize_once_and_disable_offload(monkeypatch): llama_model_default_params=lambda: SimpleNamespace(n_gpu_layers=-1), ) monkeypatch.setattr(llamacpp_backend, "_BACKEND_INITIALIZED", False) - first = llamacpp_backend._cpu_model_params(library) - second = llamacpp_backend._cpu_model_params(library) + cpu = llamacpp_backend._model_params(library, 0) + gpu = llamacpp_backend._model_params(library, 99) assert calls == ["init"] - assert first.n_gpu_layers == second.n_gpu_layers == 0 + assert cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 def test_restore_state_rejects_native_failure(): @@ -183,8 +188,9 @@ def test_real_gguf_scores_direct_serial_and_shared(): revision = os.environ.get( "SEMIF_LLAMACPP_REVISION", "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a") threads = int(os.environ.get("SEMIF_LLAMACPP_THREADS", "8")) + gpu_layers = int(os.environ.get("SEMIF_LLAMACPP_GPU_LAYERS", "0")) model, tokenizer, metadata = llamacpp_backend.load_model( - source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads) + source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads, gpu_layers=gpu_layers) state = "The deployment completed at 14:02 UTC. Health checks passed in all three zones." rows = [ { @@ -215,8 +221,156 @@ def test_real_gguf_scores_direct_serial_and_shared(): for serial_result, shared_result in zip(serial, shared): numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"]) assert metadata["backend"] == "llamacpp" - assert metadata["n_gpu_layers"] == 0 + assert metadata["n_gpu_layers"] == gpu_layers assert metadata["max_prompt_tokens"] == 4096 assert metadata["context_tokens"] >= metadata["max_prompt_tokens"] finally: model.close() + + # The same decisions through copied-sequence fan-out must match the restore path. + model, tokenizer, metadata = llamacpp_backend.load_model( + source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads, sequences=3, + gpu_layers=gpu_layers) + try: + assert metadata["n_seq_max"] == 3 + fanned, timing = llamacpp_backend.score_shared(model, tokenizer, rows, metadata) + assert timing["serving_config"] == "llamacpp-seq-copy-parallel-v1" + assert timing["branch_state_bytes"] == 0 + assert choices(fanned) == ["yes", "yes"] + for shared_result, fanned_result in zip(shared, fanned): + numpy.testing.assert_allclose( + shared_result["option_logits"], fanned_result["option_logits"], rtol=0, atol=0.5) + marginal_meta = {**metadata, "readout_mode": "marginal"} + folded, timing = llamacpp_backend.score_shared(model, tokenizer, rows, marginal_meta) + assert timing["serving_config"] == "llamacpp-seq-copy-parallel-marginal-v1" + assert choices(folded) == ["yes", "yes"] + assert all(0.0 <= result["preamble_mass"] <= 1.0 for result in folded) + finally: + model.close() + + +def test_load_model_validates_new_arguments(tmp_path): + from semif_phase1 import llamacpp_backend + + weights = tmp_path / "model.gguf" + weights.write_bytes(b"gguf") + revision = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" + with pytest.raises(ValueError, match="gpu_layers"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-1) + with pytest.raises(ValueError, match="sequences"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) + with pytest.raises(ValueError, match="readout"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, readout="first") + + +def _fake_engine_for_fanout(sequences: int, vocab_size: int = 8): + """An _Engine whose native calls are recorded instead of executed.""" + from semif_phase1.llamacpp_backend import _Engine + + calls = {"seq_cp": [], "seq_rm": [], "batches": []} + + class Batch: + def __init__(self, n, seqs): + self.token = [0] * n + self.pos = [0] * n + self.n_seq_id = [0] * n + self.seq_id = [[0] * seqs for _ in range(n)] + self.logits = [0] * n + self.n_tokens = 0 + + def decode(context, batch): + calls["batches"].append([(batch.token[i], batch.pos[i], batch.seq_id[i][0], batch.logits[i]) + for i in range(batch.n_tokens)]) + return 0 + + def logits_ith(context, index): + # Encode the branch id in the logits so the caller can tell rows apart. + batch = calls["batches"][-1] + branch = batch[index][2] + array = (ctypes_float * vocab_size)(*([0.0] * vocab_size)) + array[branch] = 10.0 + return array + + import ctypes as _ctypes + ctypes_float = _ctypes.c_float + library = SimpleNamespace( + llama_batch_init=lambda n, embd, seqs: Batch(n, seqs), + llama_batch_free=lambda batch: None, + llama_decode=decode, + llama_get_logits_ith=logits_ith, + llama_memory_seq_cp=lambda memory, src, dst, p0, p1: calls["seq_cp"].append((src, dst, p0, p1)), + llama_memory_seq_rm=lambda memory, seq, p0, p1: calls["seq_rm"].append((seq, p0, p1)) or True, + ) + engine = _Engine.__new__(_Engine) + engine.lib = library + engine.context = object() + engine.memory = object() + engine.sequences = sequences + engine.vocab_size = vocab_size + return engine, calls + + +def test_fanout_copies_prefix_decodes_once_and_removes_branches_whole(): + engine, calls = _fake_engine_for_fanout(sequences=4) + vocabularies = engine.fanout_logits(prefix_length=10, suffixes=[[5, 6], [7], [8, 9, 3]]) + # one copy of sequence 0 per branch, whole range + assert calls["seq_cp"] == [(0, 1, -1, -1), (0, 2, -1, -1), (0, 3, -1, -1)] + # a single decode call carrying every suffix token, positions continuing the prefix + assert len(calls["batches"]) == 1 + batch = calls["batches"][0] + assert [(t, p, s) for t, p, s, _ in batch] == [ + (5, 10, 1), (6, 11, 1), (7, 10, 2), (8, 10, 3), (9, 11, 3), (3, 12, 3)] + # logits requested only at each branch's last token + assert [want for _, _, _, want in batch] == [0, 1, 1, 0, 0, 1] + # each returned row belongs to its branch + assert [int(v.argmax()) for v in vocabularies] == [1, 2, 3] + # branches removed whole afterwards, sequence 0 untouched + assert calls["seq_rm"] == [(1, -1, -1), (2, -1, -1), (3, -1, -1)] + + +def test_fanout_refuses_more_branches_than_slots(): + engine, _ = _fake_engine_for_fanout(sequences=2) + with pytest.raises(ValueError, match="exceed"): + engine.fanout_logits(3, [[1], [2]]) + + +def test_marginal_readout_folds_preamble_mass_into_slots(): + from semif_phase1.llamacpp_backend import _marginal_readout + + probed = {} + + class Engine: + def probe_branches(self, positions, tokens): + probed.update(tokens) + # After the preamble token the model answers slot 1 (token id 1) with certainty. + out = numpy.full(8, -20.0) + out[1] = 20.0 + return {branch: out.copy() for branch in tokens} + + # Branch 1: the model puts its mass on token 7 (a preamble), almost none on the slots 0 and 1. + vocabulary = numpy.full(8, -20.0) + vocabulary[7] = 20.0 + vocabulary[0] = 1.0 + vocabulary[1] = 0.0 + (logits, preamble_mass), = _marginal_readout( + Engine(), branches=[1], prefix_length=10, suffixes=[[4, 4]], + vocabularies=[vocabulary], slot_lists=[[0, 1]]) + assert probed == {1: 7} + # Direct read alone would have picked slot 0; the preamble path moves the answer to slot 1. + assert int(numpy.argmax(logits)) == 1 + assert preamble_mass > 0.99 + + +def test_marginal_readout_skips_branches_that_already_answer_with_a_slot(): + from semif_phase1.llamacpp_backend import _marginal_readout + + class Engine: + def probe_branches(self, positions, tokens): + raise AssertionError("no probe should be issued") + + vocabulary = numpy.full(8, -20.0) + vocabulary[0] = 20.0 # slot 0 is the confident next token; nothing else clears the floor + (logits, preamble_mass), = _marginal_readout( + Engine(), branches=[1], prefix_length=3, suffixes=[[9]], + vocabularies=[vocabulary], slot_lists=[[0, 1]]) + assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 From c0fbc50cbba5b8ba6b2af7573e7251aeb7ad934f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 07:37:35 +0200 Subject: [PATCH 2/8] llamacpp: size the fan-out from the data, offload by default; GGUF results on a laptop GPU --llama-parallel and --llama-gpu-layers now default to "auto". For the layers that means not overriding llama.cpp's own default (-1, every layer, in current builds) and recording what the library did; the original backend forced 0. For the branches it means that nothing is asked of the user: the rows are already tokenized by the time shared mode runs, so the prefix length and every suffix length are known, and each state's fan-out holds as many questions as prefix + sum(suffixes) <= n_ctx allows, 32 at most. The context is sized once, for the longest single prompt, not multiplied by the sequence count: with a unified KV buffer every sequence sees the whole n_ctx (measured on this build, 16 sequences and n_ctx 4096 give 4096 per sequence against 256 without), and copied branches share the prefix cells. branches_per_decode in the shared timing records what was done. An integer still fixes n_seq_max; 1 keeps the state-restore path. Results on an RTX 3080 Laptop with the pinned Q4_K_M GGUF, all layers offloaded: authored144 direct 0.796 mean family balanced accuracy (Torch BF16 on a 3090: 0.813), ECE 0.063 out of fold (0.038), median allowed_token_mass 0.9997; shape777 1.40 / 9.21 / 10.88 decisions per second fresh, serial and parallel with 8 branches, 10.51 with auto sizing (Torch: 2.33 / 10.75 / 20.03), 16 to 19 argmax flips out of 777 between decode paths, which is this quantized model's noise floor. One decode per state is no faster than three: llama.cpp re-splits into n_ubatch micro-batches either way. Raw reports, predictions and calibration are committed with their checksums; phase-1 claims are untouched and verify_published.py passes. The real-GGUF test compared serial and shared logits with an exact tolerance that only held because both were the restore path; it now requires exactness when n_seq_max == 1 and 0.5 logits otherwise, decisions still equal. It passes on the PyPI CPU wheel and on the cu124 wheel with offload. --- README.md | 5 +- benchmarks/README.md | 12 + benchmarks/shape777.py | 22 +- docs/LLAMACPP.md | 85 +- docs/RESULTS.md | 22 + results/raw/SHA256SUMS | 7 + .../llamacpp-gguf-cuda-authored144.json | 194 ++ .../raw/llamacpp-gguf-cuda-authored144.json | 833 ++++++ ...lamacpp-gguf-cuda-direct-authored144.jsonl | 144 + .../raw/shape777-llamacpp-gguf-cuda-auto.json | 104 + ...-llamacpp-gguf-cuda-auto.predictions.jsonl | 2331 +++++++++++++++++ results/raw/shape777-llamacpp-gguf-cuda.json | 100 + ...pe777-llamacpp-gguf-cuda.predictions.jsonl | 2331 +++++++++++++++++ src/semif_phase1/cli.py | 35 +- src/semif_phase1/llamacpp_backend.py | 105 +- tests/test_llamacpp.py | 56 +- 16 files changed, 6323 insertions(+), 63 deletions(-) create mode 100644 results/raw/calibration/llamacpp-gguf-cuda-authored144.json create mode 100644 results/raw/llamacpp-gguf-cuda-authored144.json create mode 100644 results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl create mode 100644 results/raw/shape777-llamacpp-gguf-cuda-auto.json create mode 100644 results/raw/shape777-llamacpp-gguf-cuda-auto.predictions.jsonl create mode 100644 results/raw/shape777-llamacpp-gguf-cuda.json create mode 100644 results/raw/shape777-llamacpp-gguf-cuda.predictions.jsonl diff --git a/README.md b/README.md index 72b7778..44c24f6 100644 --- a/README.md +++ b/README.md @@ -58,8 +58,9 @@ pip install -e '.[test]' checkpoint, on CPU or with `--llama-gpu-layers` offloaded to a GPU. Install `pip install -e '.[test,llamacpp]'`, download a GGUF of the pinned model (for example `bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf /path/to/model.gguf`. -Shared mode can fan its questions out over copied sequences in one batched decode with -`--llama-parallel N`, which also works on hybrid Qwen3.5 memories. Prompt hashes match the +Layers are offloaded by default when the wheel can, and shared mode fans a state's questions +out over copied sequences in as few batched decodes as the context allows — sized from the +rows themselves, which also works on hybrid Qwen3.5 memories. Prompt hashes match the Torch backend; quantized option scores have small numerical differences. See [docs/LLAMACPP.md](docs/LLAMACPP.md). diff --git a/benchmarks/README.md b/benchmarks/README.md index 4e5123d..693bfc2 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -48,6 +48,18 @@ CUDA_VISIBLE_DEVICES=0 python benchmarks/shape777_reranker.py \ --output shape777-reranker-run.json ``` +The same fixture on a local GGUF through llama.cpp, layers offloaded by default when the wheel +can, branches sized per state: + +```bash +python benchmarks/shape777.py --backend llamacpp \ + --model Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/shape777.jsonl \ + --output shape777-llamacpp-run.json +``` + The 6.7 MB fixture is project-authored and has SHA-256 `8dcf414b12fc2684e3c4ca5f3ebfd3f525f5346fec4a9bc67eb65138101f55f1`. Both runners write aggregate timings and row-level predictions. ## Quality evidence diff --git a/benchmarks/shape777.py b/benchmarks/shape777.py index cb9dd0e..6195d7a 100644 --- a/benchmarks/shape777.py +++ b/benchmarks/shape777.py @@ -35,19 +35,24 @@ def main() -> None: parser.add_argument("--backend", choices=("torch", "llamacpp"), default="torch") parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp") - parser.add_argument("--llama-gpu-layers", type=int, default=0, - help="Layers offloaded to the GPU for --backend llamacpp (default: 0)") - parser.add_argument("--llama-parallel", type=int, default=1, - help="Branch slots for llama.cpp shared mode (default: 1 = state restore)") + parser.add_argument("--llama-gpu-layers", default="auto", + help="GPU layers for --backend llamacpp: 'auto' (library default), 0 (CPU) or N") + parser.add_argument("--llama-parallel", default="auto", + help="llama.cpp shared-mode branching: 'auto' (sized per state), N, or 1 (state restore)") args = parser.parse_args() if args.output.exists(): parser.error("Output must be new") if args.backend == "llamacpp": if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_gpu_layers < 0 or args.llama_parallel < 1: - parser.error("--llama-gpu-layers must be nonnegative and --llama-parallel positive") - elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers or args.llama_parallel != 1: + for name in ("llama_gpu_layers", "llama_parallel"): + value = getattr(args, name) + if value != "auto": + try: + setattr(args, name, int(value)) + except ValueError: + parser.error(f"--{name.replace('_', '-')} must be 'auto' or an integer") + elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers != "auto" or args.llama_parallel != "auto": parser.error("llama.cpp options require --backend llamacpp") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] groups = defaultdict(list) @@ -66,7 +71,8 @@ def main() -> None: SerialPrefixScorer = llamacpp_backend.SerialPrefixScorer score_shared = llamacpp_backend.score_shared cuda = None - hardware = (_gpu_name() or "unknown GPU") if args.llama_gpu_layers else "CPU" + offloaded = metadata["n_gpu_layers"] != 0 and metadata["gpu_offload_supported"] + hardware = (_gpu_name() or "unknown GPU") if offloaded else "CPU" else: model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") import torch as cuda diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 51aa377..20bfb0d 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -32,9 +32,11 @@ Each prediction records the file's size and checksum. ### GPU offload -`--llama-gpu-layers N` offloads `N` layers (use a large value such as `99` for -all of them). It needs a llama-cpp-python wheel built with a GPU backend; the -PyPI wheel is CPU-only. For CUDA: +`--llama-gpu-layers` defaults to `auto`, which leaves llama.cpp's own default — +`-1`, every layer, in current builds — and records what the library did. `0` +forces CPU, `N` offloads `N` layers. Offload needs a llama-cpp-python wheel +built with a GPU backend; the PyPI wheel is CPU-only and ignores the setting +(`gpu_offload_supported` in the metadata says so). For CUDA: ```bash pip install --force-reinstall --no-deps "llama-cpp-python==0.3.35" \ @@ -55,8 +57,9 @@ ways to make a branch: | `--llama-parallel` | branching | decodes per state | |---|---|---| -| `1` (default) | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | -| `N ≥ 2` | **sequence copy** — `llama_memory_seq_cp` of sequence 0 into up to `N − 1` throwaway sequences, all suffixes in one batched `llama_decode` | one per `N − 1` questions | +| `auto` (default) | **sequence copy, sized per state** — as many of the state's questions per decode as the context holds: `prefix + Σ suffixes ≤ n_ctx`, at most 32 branches | usually one | +| `N ≥ 2` | sequence copy with `n_seq_max = N`, chunks of at most `N − 1` | one per `N − 1` questions | +| `1` | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | State restore is what the backend has always done. Sequence copy is the llama.cpp equivalent of the Torch backend's `native-state-prefix-parallel-v1`: @@ -65,6 +68,21 @@ suffixes share a single decode. The branches are removed whole afterwards (whole-sequence removal never fails), so sequence 0 is left exactly as prefilled and the next chunk of questions needs no restore at all. +Why `auto` needs no number from you: everything that bounds a fan-out is known +before the first decode. `encode_verified` has already tokenized each row, so +the prefix length and every suffix length are in hand, and the context budget +is fixed at load. The branch count is therefore a consequence of the data — on +the `shape777` fixture (states of ~1 800 tokens, suffixes of ~90) all 21 +questions of a state fit in one decode. `branches_per_decode` in the shared +timing records what was actually done. + +The context is sized **once**, for the longest single prompt (`max_tokens + 64`), +not multiplied by the number of sequences. That relies on `kv_unified`: measured +on this build, with 16 sequences and `n_ctx = 4096`, each sequence sees 4 096 +cells under the unified buffer and 256 without it, and copied branches share the +prefix's cells rather than duplicating them. A library without `kv_unified` falls +back to the per-sequence allocation. + ### Hybrid models Qwen3.5 is a hybrid architecture: one full-attention layer in four, the rest @@ -82,10 +100,7 @@ is why both branching modes above work on Qwen3.5. Pure-attention models additionally allow tail truncation; the backend does not rely on it. `n_seq_max` is a context parameter (`llama_context_params.n_seq_max`), not a -build option; the same wheel serves both modes. The context is allocated as -`n_seq_max × (max_tokens + 64)` tokens and, when the library exposes it, -`kv_unified` is enabled so the copied branches share the prefix's attention -cells. +build option; the same wheel serves both modes. ## Validation @@ -107,7 +122,7 @@ model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. ## Marginal readout -`--llama-readout marginal` (shared mode, `--llama-parallel ≥ 2`) changes how +`--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how the answer is read, not what is asked. Some checkpoints want to emit a token *before* the letter — Qwen3-8B puts almost all of its next-token mass on the Markdown `**` token when the assistant turn starts empty, and reading only the @@ -123,14 +138,58 @@ changes nothing and `preamble_mass` stays at zero. ## Results - +RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` 0.3.35 cu124. + +### Quality — `authored144`, direct mode + +| backend | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | +|---|---:|---:|---:| +| Torch BF16, RTX 3090 (committed) | 0.813 | 0.038 | 1.23 | +| **llama.cpp GGUF Q4_K_M, laptop** | **0.796** | **0.063** [0.038, 0.123] | 1.26 | + +Coverage 144/144. Median `allowed_token_mass` 0.9997, minimum 0.975, no row +below 0.9 — the quantized model answers with a letter as reliably as the BF16 +one. Median forward time 64 ms per decision at a median 147 input tokens. +Row-level predictions are in +`results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl`, the +report in `results/raw/llamacpp-gguf-cuda-authored144.json`, the calibration +in `results/raw/calibration/llamacpp-gguf-cuda-authored144.json`. The 4-bit +checkpoint gives up 1.7 points of balanced accuracy and is somewhat less +well-calibrated than BF16; its fitted temperature is nearly the same. + +### Systems — `shape777`, 37 states × 21 questions + +| mode | decisions / s | state p50 | argmax flips vs fresh | +|---|---:|---:|---:| +| fresh | 1.40 | 14.97 s | — | +| serial_prefix (state restore) | 9.21 | 2.28 s | 19 / 777 | +| parallel_shared, `--llama-parallel 8` | 10.88 | 1.93 s | 16 / 777 | +| parallel_shared, `auto` | 10.51 | 2.00 s | 18 / 777 | + +The committed Torch BF16 run on an RTX 3090 reports 2.33, 10.75 and 20.03 +decisions per second for fresh, serial and parallel. + +Two things the numbers say. Fan-out beats state restore by removing the +serialization round-trip, not by batching: one decode per state (`auto`, +21 branches) is no faster than three (8 branches), because llama.cpp splits a +batch into micro-batches of `n_ubatch` tokens either way and the recurrent +memory decodes them with `split_equal`. And the gap to the Torch parallel figure +is not the fan-out's: the Torch backend runs one dense BF16 forward over padded +suffixes, which this 4-bit hybrid path cannot match on a laptop GPU. Reports: +`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches) and +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, each with its +`.predictions.jsonl`. The 16–19 argmax flips out of 777 between decode paths +are the quantized model's noise floor — two sequential reads of one prompt +already differ on about 3 % of decisions when only the micro-batch size +changes — not a property of the fan-out. + ## Reproduce ```bash # Quality: the owned labeled workload, three serving configurations for MODE in direct serial shared; do - semif-score --backend llamacpp --mode $MODE --llama-gpu-layers 99 --llama-parallel 8 \ + semif-score --backend llamacpp --mode $MODE \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ --input benchmarks/data/authored144.jsonl \ @@ -140,7 +199,7 @@ python benchmarks/evaluate.py --gold benchmarks/data/authored144.jsonl \ --predictions results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl # Systems: the 37x21 fixture, fresh / serial-prefix / parallel-shared -python benchmarks/shape777.py --backend llamacpp --llama-gpu-layers 99 --llama-parallel 8 \ +python benchmarks/shape777.py --backend llamacpp \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ --input benchmarks/data/shape777.jsonl \ diff --git a/docs/RESULTS.md b/docs/RESULTS.md index bb0d7d6..9aa2dcb 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -84,3 +84,25 @@ Not reproduced or established: - The full 711-row TypeSafe benchmark or an independently operated Jev endpoint. The next justified phase is targeted training for decision semantics and calibration, judged against these frozen baselines. It should proceed only after expanding external gold tasks and defining a held-out operational calibration target. A generic reranker fine-tune would answer the wrong question. + +## Appendix — llama.cpp GGUF on a laptop GPU + +Added with the GPU/fan-out extension of the llama.cpp backend; the phase-1 +claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, +3 013 027 808 bytes), all layers on an RTX 3080 Laptop (16 GB), `llama-cpp-python` +0.3.35 cu124. + +| workload | metric | Torch BF16, 3090 | GGUF Q4_K_M, laptop | +|---|---|---:|---:| +| authored144, direct | mean family balanced accuracy | 0.813 | 0.796 | +| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.063 | +| shape777, fresh | decisions / s | 2.33 | 1.40 | +| shape777, serial prefix | decisions / s | 10.75 | 9.21 | +| shape777, parallel shared | decisions / s | 20.03 | 10.88 (8 branches) · 10.51 (auto) | +| shape777, parallel vs fresh | argmax flips / 777 | 6 | 16–18 | + +Raw: `results/raw/llamacpp-gguf-cuda-authored144.json`, +`results/raw/calibration/llamacpp-gguf-cuda-authored144.json`, +`results/raw/shape777-llamacpp-gguf-cuda.json`, +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, with row-level predictions +beside them. Method and caveats in [LLAMACPP.md](LLAMACPP.md). diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 24c0876..6562fe4 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -21,3 +21,10 @@ b24a574ce11a8a73b643cbd35c2ef49bc66e0ebde0e6d622f8a17602d37b40ee calibration/di 2805185a19bdadb61f4198a3f1fa6e88bc9c1a34c3011fcff9e66f8b23f7d4b3 calibration/direct-wanli256.calibrated.jsonl fecbcd6fbce4fc80c3fcc589771a7bb0bfc592e678b496868ca62015265a4a47 calibration/direct-every204.calibrated.jsonl 161d013089fc496981913c055f9ad5878b6308bda19feca36b8be0e9cc2b7e7d calibration/summary.json +9955efe42cd33f096f26f39259ebebd4718bce7f777f3ec3e8bcb47e1203afc1 predictions/llamacpp-gguf-cuda-direct-authored144.jsonl +616831384c6d072505ba080f7cd5d792385c24d05f92361c26158765697c1728 llamacpp-gguf-cuda-authored144.json +690f1ad6a4ab0af2457489ed47bfa5c0ad1a57f26b2c839f50abd9b3a1fbe975 calibration/llamacpp-gguf-cuda-authored144.json +fbdf611fb21ee4a01d219d9a321ffe34ed04c264412cbaae89febe62a398a4e9 shape777-llamacpp-gguf-cuda.json +4d68ac23eaa09a3f43a16fc3cb212539451d4b17fcc4e221b4f3387a5bcad0f8 shape777-llamacpp-gguf-cuda.predictions.jsonl +cc782460f61ffa3ef126b9b702832a2eede6c9cc02a170a76bd059d378e55fca shape777-llamacpp-gguf-cuda-auto.json +bf070210ae2b83ce1ebe2f29ab2127eb04620a48d70326df01b872c17c2cc479 shape777-llamacpp-gguf-cuda-auto.predictions.jsonl diff --git a/results/raw/calibration/llamacpp-gguf-cuda-authored144.json b/results/raw/calibration/llamacpp-gguf-cuda-authored144.json new file mode 100644 index 0000000..6df9a71 --- /dev/null +++ b/results/raw/calibration/llamacpp-gguf-cuda-authored144.json @@ -0,0 +1,194 @@ +{ + "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", + "gold": "authored144.jsonl", + "predictions": "llamacpp-gguf-cuda-direct-authored144.jsonl", + "rows": 144, + "groups": 36, + "shipped_temperature": 1.256836787269229, + "cv": 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"9ca2d99a18f5d41ad08d", "option_ids": ["yes", "no"], "probabilities": [0.6010411920068965, 0.39895880799310346], "option_logits": [25.589309692382812, 25.17950439453125], "answer_token_ids": [32, 33], "input_tokens": 1853, "allowed_token_mass": 0.9994217173841765, "full_vocab_argmax_id": 32, "prompt_sha256": "a67b36f1653e4b97791b75f71210200bcc8830ac3c06dfba2aabced801aea950", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "dcd13ec9cee28c9f9854", "option_ids": ["yes", "no"], "probabilities": [0.1256134369567277, 0.8743865630432722], "option_logits": [24.843854904174805, 26.784168243408203], "answer_token_ids": [32, 33], "input_tokens": 1852, "allowed_token_mass": 0.9996767064989092, "full_vocab_argmax_id": 33, "prompt_sha256": "830fd92238c6217ebff0cef76ad51cf11fd23ecbbf7893812d3cacb7c96573d0", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "39685613525e4d5d1189", "option_ids": ["yes", "no"], "probabilities": [0.020912979433086017, 0.979087020566914], "option_logits": [23.330917358398438, 27.177167892456055], "answer_token_ids": [32, 33], "input_tokens": 1858, "allowed_token_mass": 0.9997578635222146, "full_vocab_argmax_id": 33, "prompt_sha256": "1cea6b1a689ddf7e482af85512404d7fd3c0eb5cb4d268438cfd189f14627cfc", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "23413859c5b32174137b", "option_ids": ["yes", "no"], "probabilities": [0.040517968107847725, 0.9594820318921522], "option_logits": [23.74753189086914, 26.912179946899414], "answer_token_ids": [32, 33], "input_tokens": 1855, "allowed_token_mass": 0.9996839076064133, "full_vocab_argmax_id": 33, "prompt_sha256": "18f6f2e36f63abf5250f2a68833332dcec926e53b18e2c69eca69c879678c79e", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "9525d3025bc8e0c5b0ff", "option_ids": ["yes", "no"], "probabilities": [0.033339528718556756, 0.9666604712814433], "option_logits": [24.263511657714844, 27.630615234375], "answer_token_ids": [32, 33], "input_tokens": 1854, "allowed_token_mass": 0.9998200321357571, "full_vocab_argmax_id": 33, "prompt_sha256": "afdf30360786f113390987a0d8d73e8d9681e54f4dbb8589fdce3689f9a9c52f", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "82fff92fa614d407d774", "option_ids": ["yes", "no"], "probabilities": [0.06491372902622361, 0.9350862709737764], "option_logits": [24.328189849853516, 26.995769500732422], "answer_token_ids": [32, 33], "input_tokens": 1852, "allowed_token_mass": 0.9997110948072204, "full_vocab_argmax_id": 33, "prompt_sha256": "54c79b410f690460b6b7e3650ed166c8b953b2b13a5a97fe6e9f2baa5b240453", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "94a5b148e1fac2d93c8a", "option_ids": ["yes", "no"], "probabilities": [0.17142635191927347, 0.8285736480807266], "option_logits": [24.517370223999023, 26.09292221069336], "answer_token_ids": [32, 33], "input_tokens": 1855, "allowed_token_mass": 0.9994954229757697, "full_vocab_argmax_id": 33, "prompt_sha256": "f058b46ffd614abefd43094517f2d57d7581d8f0a34f001189bd528d727e29e6", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index 2b99936..303a22f 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -23,11 +23,13 @@ def main() -> None: parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp (default: all visible cores)") - parser.add_argument("--llama-gpu-layers", type=int, default=0, - help="Layers to offload to the GPU for --backend llamacpp (default: 0, CPU only)") - parser.add_argument("--llama-parallel", type=int, default=1, - help="Branch slots for --backend llamacpp shared mode: suffixes are fanned out over " - "copied sequences and decoded together (default: 1 = state restore per decision)") + parser.add_argument("--llama-gpu-layers", default="auto", + help="GPU layers for --backend llamacpp: 'auto' keeps llama.cpp's default (every layer " + "when the library can offload), 0 forces CPU, N offloads N layers") + parser.add_argument("--llama-parallel", default="auto", + help="Shared-mode branching for --backend llamacpp: 'auto' sizes each fan-out from the " + "state's token counts over a unified KV buffer; N fixes n_seq_max; 1 restores " + "state per decision") parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " "over one-token preambles (needs --llama-parallel > 1)") @@ -59,8 +61,21 @@ def main() -> None: parser.error("--llama-threads must be positive") if args.backend == "mlx" and args.mode == "reranker": parser.error("MLX supports direct, serial, and shared modes; reranker requires torch") + def _auto_or_int(name, value, minimum): + if value == "auto": + return "auto" + try: + number = int(value) + except (TypeError, ValueError): + parser.error(f"{name} must be 'auto' or an integer") + if number < minimum: + parser.error(f"{name} must be 'auto' or at least {minimum}") + return number + + args.llama_gpu_layers = _auto_or_int("--llama-gpu-layers", args.llama_gpu_layers, -1) + args.llama_parallel = _auto_or_int("--llama-parallel", args.llama_parallel, 1) for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): - if args.backend != "llamacpp" and value != parser.get_default(name.lstrip("-").replace("-", "_")): + if args.backend != "llamacpp" and value != "auto": parser.error(f"{name} requires --backend llamacpp") if args.llama_readout != "last" and args.backend != "llamacpp": parser.error("--llama-readout requires --backend llamacpp") @@ -69,12 +84,8 @@ def main() -> None: parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_gpu_layers < 0: - parser.error("--llama-gpu-layers must be nonnegative") - if args.llama_parallel < 1: - parser.error("--llama-parallel must be positive") - if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel < 2): - parser.error("--llama-readout marginal requires --mode shared and --llama-parallel of at least 2") + if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel == 1): + parser.error("--llama-readout marginal requires --mode shared and --llama-parallel other than 1") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 446e517..9b366cc 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -42,17 +42,27 @@ DECODE_CHUNK = 512 READOUTS = ("last", "marginal") +AUTO = "auto" +#: Upper bound on branches per fan-out when `sequences` is "auto". With a unified KV buffer the +#: bound costs bookkeeping only; the real limit is the token budget, checked per state. +BRANCH_CAP = 32 _BACKEND_INITIALIZED = False -def _model_params(library, gpu_layers: int): - """Initialize llama.cpp once and return model parameters with the requested GPU offload.""" +def _model_params(library, gpu_layers: int | None): + """Initialize llama.cpp once and return model parameters. + + ``None`` keeps the library's own default for ``n_gpu_layers`` (``-1``, every layer, in current + builds); an integer overrides it. The value that ends up in the params is what the metadata + reports, so a run is reproducible whichever way it was chosen. + """ global _BACKEND_INITIALIZED if not _BACKEND_INITIALIZED: library.llama_backend_init() _BACKEND_INITIALIZED = True params = library.llama_model_default_params() - params.n_gpu_layers = gpu_layers + if gpu_layers is not None: + params.n_gpu_layers = gpu_layers return params @@ -100,9 +110,17 @@ def __init__(self, library, model, context_tokens: int, threads: int, sequences: if not (isinstance(sequences, int) and sequences >= 1): raise ValueError("sequences must be a positive integer") params = library.llama_context_default_params() - # Sequence 0 keeps the prefix; the others are branch slots. Without a unified KV buffer - # llama.cpp splits n_ctx across sequences, so size it for the worst case either way. - params.n_ctx = context_tokens * sequences + # Sequence 0 keeps the prefix; the others are branch slots. With a unified KV buffer every + # sequence sees the whole n_ctx (measured: 16 sequences, n_ctx 4096 -> 4096 per sequence, + # against 256 without), and copied branches share the prefix's cells, so the context is + # sized once for the longest prompt and the branch count is decided per state from the + # token budget. Without a unified buffer llama.cpp splits n_ctx across sequences, and the + # only safe sizing is the worst case. + unified = None + if sequences > 1 and hasattr(params, "kv_unified"): + params.kv_unified = True + unified = True + params.n_ctx = context_tokens if unified or sequences == 1 else context_tokens * sequences params.n_seq_max = sequences # One flagged output per sequence in a fan-out batch; llama.cpp requires # n_outputs_max >= n_seq_max * n_outputs_max_per_seq. @@ -110,11 +128,6 @@ def __init__(self, library, model, context_tokens: int, threads: int, sequences: params.n_outputs_max_per_seq = 1 params.n_threads = threads params.n_threads_batch = threads - unified = None - if sequences > 1 and hasattr(params, "kv_unified"): - # Branches share the whole prefix; llama.h recommends the unified buffer for that case. - params.kv_unified = True - unified = True self.lib = library self.model = model self.sequences = sequences @@ -309,13 +322,16 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, - context_tokens: int = 4096, gpu_layers: int = 0, sequences: int = 1, - readout: str = "last"): + context_tokens: int = 4096, gpu_layers: int | str | None = AUTO, + sequences: int | str = AUTO, readout: str = "last"): """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. - ``gpu_layers`` is passed straight to llama.cpp (0 keeps the historical CPU behaviour, a - large value offloads every layer). ``sequences`` is the context's ``n_seq_max``: with more - than one, shared scoring fans suffixes out over copied sequences instead of restoring state. + ``gpu_layers``: ``"auto"`` (or ``None``) leaves llama.cpp's default, which offloads every + layer when the library can; ``0`` forces CPU; any other integer is passed through. + + ``sequences``: ``"auto"`` opens ``1 + BRANCH_CAP`` sequences over a unified KV buffer and lets + shared scoring size each fan-out from the state's own token counts; an integer fixes + ``n_seq_max`` (``1`` keeps the historical state-restore path). """ local = Path(source).is_dir() if not local and not re.fullmatch(r"[0-9a-f]{40}", revision or ""): @@ -331,10 +347,15 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, threads = os.cpu_count() or 4 if not (isinstance(threads, int) and threads >= 1): raise ValueError("threads must be a positive integer") - if not (isinstance(gpu_layers, int) and gpu_layers >= 0): - raise ValueError("gpu_layers must be a nonnegative integer") + if gpu_layers == AUTO: + gpu_layers = None + if gpu_layers is not None and not (isinstance(gpu_layers, int) and gpu_layers >= -1): + raise ValueError("gpu_layers must be 'auto', -1, or a nonnegative integer") + auto_sequences = sequences == AUTO + if auto_sequences: + sequences = 1 + BRANCH_CAP if not (isinstance(sequences, int) and sequences >= 1): - raise ValueError("sequences must be a positive integer") + raise ValueError("sequences must be 'auto' or a positive integer") if readout not in READOUTS: raise ValueError(f"readout must be one of {READOUTS}") import transformers @@ -376,9 +397,11 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "gguf": gguf_record, "vocab_size": engine.vocab_size, "threads": threads, - "n_gpu_layers": gpu_layers, + "n_gpu_layers": int(model_params.n_gpu_layers), + "n_gpu_layers_requested": "auto" if gpu_layers is None else gpu_layers, "gpu_offload_supported": bool(llama_cpp.llama_supports_gpu_offload()), "n_seq_max": sequences, + "branch_sizing": "auto" if auto_sequences else "fixed", "kv_unified": engine.kv_unified, "readout_mode": readout, "max_prompt_tokens": context_tokens, @@ -475,6 +498,31 @@ def score(self, row: dict) -> dict: return result +def _chunks(prefix_length: int, suffix_lengths: list[int], budget: int, cap: int) -> list[list[int]]: + """Group consecutive rows into fan-out chunks from what the context can hold. + + A chunk holds at most ``cap`` branches and never more than ``budget`` tokens including the + shared prefix. Everything here is known before the first decode, so the branch count is a + consequence of the data, not a setting. + """ + if budget <= prefix_length: + raise ValueError("The state prefix alone exceeds the context") + chunks: list[list[int]] = [] + current: list[int] = [] + total = prefix_length + for index, length in enumerate(suffix_lengths): + if prefix_length + length > budget: + raise ValueError(f"Row {index}: prefix plus suffix exceed the context; raise --max-tokens") + if current and (len(current) >= cap or total + length > budget): + chunks.append(current) + current, total = [], prefix_length + current.append(index) + total += length + if current: + chunks.append(current) + return chunks + + def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, suffixes: list[list[int]], vocabularies: list[numpy.ndarray], slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): @@ -574,10 +622,16 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: branch_bytes = state_data[1] config = "llamacpp-state-restore-shared-v1" else: - width = engine.sequences - 1 - for start in range(0, len(rows), width): - chunk_rows = rows[start : start + width] - chunk_encoded = encoded[start : start + width] + # Under a unified KV buffer the copied branches share the prefix cells, so the budget is + # the context minus a small margin; otherwise each sequence has its own slice of n_ctx. + per_sequence = engine.context_tokens if engine.kv_unified else engine.context_tokens // engine.sequences + budget = per_sequence - 8 if engine.kv_unified else per_sequence + chunks = _chunks(len(prefix), [len(ids) - len(prefix) for ids, _, _ in encoded], + budget, engine.sequences - 1) + branch_counts = [len(chunk) for chunk in chunks] + for chunk in chunks: + chunk_rows = [rows[i] for i in chunk] + chunk_encoded = [encoded[i] for i in chunk] suffixes = [ids[len(prefix) :] for ids, _, _ in chunk_encoded] slot_lists = [slots for _, slots, _ in chunk_encoded] branches = list(range(1, len(suffixes) + 1)) @@ -622,6 +676,8 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: results.append(result) branch_bytes = 0 config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" + if engine.sequences == 1: + branch_counts = [1] * len(rows) suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) timing = { "total_seconds": time.perf_counter() - started, @@ -632,6 +688,7 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "suffix_forward_seconds": suffix_seconds, "batch_size": len(rows), "branch_slots": engine.sequences - 1, + "branches_per_decode": branch_counts, "branch_state_bytes": branch_bytes, "serving_config": config, "true_suffix_tokens": suffix_total, diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 3a7a48d..05e328a 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -18,6 +18,8 @@ (["--mode", "direct", "--backend", "llamacpp", "--gguf", "missing.gguf"], "requires --gguf"), (["--mode", "direct", "--llama-gpu-layers", "99"], "--llama-gpu-layers requires --backend llamacpp"), (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), + (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-parallel", "0"], "at least 1"), + (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-gpu-layers", "many"], "must be 'auto' or an integer"), (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): @@ -54,7 +56,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 - assert record["gpu_layers"] == 0 and record["sequences"] == 1 and record["readout"] == "last" + assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" and record["readout"] == "last" def test_load_model_rejects_unpinned_remote_revision(): @@ -90,10 +92,11 @@ def test_model_params_initialize_once_and_apply_offload(monkeypatch): llama_model_default_params=lambda: SimpleNamespace(n_gpu_layers=-1), ) monkeypatch.setattr(llamacpp_backend, "_BACKEND_INITIALIZED", False) + default = llamacpp_backend._model_params(library, None) cpu = llamacpp_backend._model_params(library, 0) gpu = llamacpp_backend._model_params(library, 99) assert calls == ["init"] - assert cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 + assert default.n_gpu_layers == -1 and cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 def test_restore_state_rejects_native_failure(): @@ -218,8 +221,14 @@ def test_real_gguf_scores_direct_serial_and_shared(): ] assert choices(direct) == choices(serial) == choices(shared) == ["yes", "yes"] assert [result["cache_hit"] for result in serial] == [False, True] + # With the default "auto" sizing, shared mode fans out over copied sequences while serial + # restores state: two different decode paths over the same quantized weights, which agree + # on the decision but not bit for bit (measured: ~0.2 logits, the same order as changing + # the micro-batch size). Exact equality only holds when both paths are the restore path. + exact = metadata["n_seq_max"] == 1 for serial_result, shared_result in zip(serial, shared): - numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"]) + numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"], + rtol=0 if exact else 0.0, atol=0 if exact else 0.5) assert metadata["backend"] == "llamacpp" assert metadata["n_gpu_layers"] == gpu_layers assert metadata["max_prompt_tokens"] == 4096 @@ -256,7 +265,7 @@ def test_load_model_validates_new_arguments(tmp_path): weights.write_bytes(b"gguf") revision = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" with pytest.raises(ValueError, match="gpu_layers"): - llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-1) + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-2) with pytest.raises(ValueError, match="sequences"): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) with pytest.raises(ValueError, match="readout"): @@ -374,3 +383,42 @@ def probe_branches(self, positions, tokens): Engine(), branches=[1], prefix_length=3, suffixes=[[9]], vocabularies=[vocabulary], slot_lists=[[0, 1]]) assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 + + +def test_chunks_follow_the_token_budget_and_the_cap(): + from semif_phase1.llamacpp_backend import _chunks + + # everything fits in one decode + assert _chunks(100, [10, 10, 10], budget=1000, cap=8) == [[0, 1, 2]] + # the budget splits: 100 + 400 + 400 = 900 fits, adding 400 more would not + assert _chunks(100, [400, 400, 400, 400], budget=1000, cap=8) == [[0, 1], [2, 3]] + # the cap splits + assert _chunks(100, [10] * 5, budget=1000, cap=2) == [[0, 1], [2, 3], [4]] + # a row that cannot fit even alone is an error, not a silent truncation + with pytest.raises(ValueError, match="exceed the context"): + _chunks(100, [950], budget=1000, cap=8) + with pytest.raises(ValueError, match="prefix alone"): + _chunks(1000, [1], budget=1000, cap=8) + + +def test_engine_sizes_context_once_under_unified_kv(): + from semif_phase1.llamacpp_backend import _Engine + + seen = {} + + class Params: + kv_unified = False + + library = SimpleNamespace( + llama_context_default_params=lambda: Params(), + llama_init_from_model=lambda model, params: seen.setdefault("params", params) or object(), + llama_get_memory=lambda value: object(), + llama_n_ctx=lambda value: seen["params"].n_ctx, + llama_model_get_vocab=lambda model: object(), + llama_n_vocab=lambda vocab: 100, + ) + engine = _Engine(library, object(), 4160, 4, sequences=33) + assert seen["params"].kv_unified is True + assert seen["params"].n_ctx == 4160 # not 33 x 4160: branches share the prefix cells + assert seen["params"].n_seq_max == 33 and seen["params"].n_outputs_max == 33 + assert engine.kv_unified is True From d4d26fde1a3648e24667553a13c6eb54ccfa76d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 08:24:32 +0200 Subject: [PATCH 3/8] docs: say what the marginal readout is and is not shown to do; where the backend sits relative to JevBench --- docs/LLAMACPP.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 20bfb0d..40b0393 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -122,6 +122,14 @@ model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. ## Marginal readout +*Validated for correctness, not for a metric gain.* On Qwen3.5-4B, which answers with the letter +directly, every result in this document is identical with or without it, and no committed workload +needs it. It is here for checkpoints that put their first-token mass on a preamble token; the case +that motivated it — Qwen3-8B answering `**` before the letter, 60 % of the mass leaking without it, +10 % with a marginalised read — was measured outside this repository, on a mail-triage fixture that +cannot be redistributed. + + `--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how the answer is read, not what is asked. Some checkpoints want to emit a token *before* the letter — Qwen3-8B puts almost all of its next-token mass on the @@ -184,6 +192,13 @@ already differ on about 3 % of decisions when only the micro-batch size changes — not a property of the fan-out. +## JevBench + +[JevBench](https://github.com/fstandhartinger/jevbench) runs SemIf through its `semif_direct` +adapter on the Torch backend (BF16, CUDA). This backend is what would let that adapter run the +pinned GGUF on a laptop GPU with shared states fanned out; it has not been run on the full public +set yet. + ## Reproduce ```bash From c550dd55e8e21da64f521852448fda80077aba9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 09:55:00 +0200 Subject: [PATCH 4/8] docs: the reproduce loop ran shared mode on a multi-state file; drop it --- docs/LLAMACPP.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 40b0393..f657749 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -202,8 +202,8 @@ set yet. ## Reproduce ```bash -# Quality: the owned labeled workload, three serving configurations -for MODE in direct serial shared; do +# Quality: the owned labeled workload, direct and serial (shared needs one state per file) +for MODE in direct serial; do semif-score --backend llamacpp --mode $MODE \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ From 01649b8945e37ce896def3524fe1637e69f6debb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 10:22:04 +0200 Subject: [PATCH 5/8] results: Torch BF16 on the same laptop GPU, with and without the fast DeltaNet kernel, next to the GGUF rows --- docs/LLAMACPP.md | 44 +- docs/RESULTS.md | 21 +- results/raw/SHA256SUMS | 7 + ...torch-bf16-rtx3080-laptop-authored144.json | 194 ++ ...16-rtx3080-laptop-direct-authored144.jsonl | 144 + ...bf16-rtx3080-laptop-reference-kernels.json | 63 + ...laptop-reference-kernels.predictions.jsonl | 2331 +++++++++++++++++ .../shape777-torch-bf16-rtx3080-laptop.json | 55 + ...orch-bf16-rtx3080-laptop.predictions.jsonl | 2331 +++++++++++++++++ ...torch-bf16-rtx3080-laptop-authored144.json | 768 ++++++ 10 files changed, 5934 insertions(+), 24 deletions(-) create mode 100644 results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json create mode 100644 results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.json create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop.json create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop.predictions.jsonl create mode 100644 results/raw/torch-bf16-rtx3080-laptop-authored144.json diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index f657749..dcf4a2a 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -150,10 +150,11 @@ RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` ### Quality — `authored144`, direct mode -| backend | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | -|---|---:|---:|---:| -| Torch BF16, RTX 3090 (committed) | 0.813 | 0.038 | 1.23 | -| **llama.cpp GGUF Q4_K_M, laptop** | **0.796** | **0.063** [0.038, 0.123] | 1.26 | +| backend | GPU | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | +|---|---|---:|---:|---:| +| Torch BF16 (committed) | RTX 3090 | 0.813 | 0.038 | 1.23 | +| Torch BF16, same laptop | RTX 3080 Laptop | 0.813 | 0.050 [0.038, 0.114] | 1.25 | +| **llama.cpp GGUF Q4_K_M** | RTX 3080 Laptop | **0.796** | **0.063** [0.038, 0.123] | 1.26 | Coverage 144/144. Median `allowed_token_mass` 0.9997, minimum 0.975, no row below 0.9 — the quantized model answers with a letter as reliably as the BF16 @@ -167,15 +168,23 @@ well-calibrated than BF16; its fitted temperature is nearly the same. ### Systems — `shape777`, 37 states × 21 questions -| mode | decisions / s | state p50 | argmax flips vs fresh | -|---|---:|---:|---:| -| fresh | 1.40 | 14.97 s | — | -| serial_prefix (state restore) | 9.21 | 2.28 s | 19 / 777 | -| parallel_shared, `--llama-parallel 8` | 10.88 | 1.93 s | 16 / 777 | -| parallel_shared, `auto` | 10.51 | 2.00 s | 18 / 777 | - -The committed Torch BF16 run on an RTX 3090 reports 2.33, 10.75 and 20.03 -decisions per second for fresh, serial and parallel. +| backend, GPU | fresh | serial prefix | parallel shared | flips vs fresh | +|---|---:|---:|---:|---:| +| Torch BF16, RTX 3090 (committed) | 2.33 | 10.75 | 20.03 | 6 / 777 | +| Torch BF16, RTX 3080 Laptop, `flash-linear-attention` | 1.51 | 10.19 | 14.14 | 3 / 777 | +| Torch BF16, RTX 3080 Laptop, reference PyTorch kernels | 1.10 | 7.78 | 10.24 | 9 / 777 | +| **llama.cpp Q4_K_M, RTX 3080 Laptop**, `--llama-parallel 8` | **1.40** | **9.21** | **10.88** | 16 / 777 | +| llama.cpp Q4_K_M, RTX 3080 Laptop, `auto` | | | 10.51 | 18 / 777 | + +Decisions per second; state p50 for the GGUF: 14.97 s fresh, 2.28 s serial, +1.93 s parallel. The two Torch rows on the laptop differ only by the Gated +DeltaNet kernels: `pip install -e .` leaves `transformers` on its reference +PyTorch implementation (it says so at load time); with `flash-linear-attention` +installed the delta rule runs in Triton. `causal_conv1d` needs `nvcc` and was +not installed. Neither kernel touches llama.cpp, which has its own ggml +operators for these layers. On the same GPU, BF16 with the fast kernel is 8 %, +11 % and 30 % faster than the 4-bit GGUF in the three modes; the GGUF trades +that for 3 GB of weights instead of 8.8 and an 8.8 GB CUDA peak instead of 11.8. Two things the numbers say. Fan-out beats state restore by removing the serialization round-trip, not by batching: one decode per state (`auto`, @@ -184,9 +193,12 @@ batch into micro-batches of `n_ubatch` tokens either way and the recurrent memory decodes them with `split_equal`. And the gap to the Torch parallel figure is not the fan-out's: the Torch backend runs one dense BF16 forward over padded suffixes, which this 4-bit hybrid path cannot match on a laptop GPU. Reports: -`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches) and -`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, each with its -`.predictions.jsonl`. The 16–19 argmax flips out of 777 between decode paths +`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches), +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, +`results/raw/shape777-torch-bf16-rtx3080-laptop.json` and +`…-reference-kernels.json`, each with its `.predictions.jsonl`; the laptop +BF16 quality run is `results/raw/torch-bf16-rtx3080-laptop-authored144.json` +with its calibration report and predictions. The 16–19 argmax flips out of 777 between decode paths are the quantized model's noise floor — two sequential reads of one prompt already differ on about 3 % of decisions when only the micro-batch size changes — not a property of the fan-out. diff --git a/docs/RESULTS.md b/docs/RESULTS.md index 9aa2dcb..0f25f6e 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -92,14 +92,19 @@ claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, 3 013 027 808 bytes), all layers on an RTX 3080 Laptop (16 GB), `llama-cpp-python` 0.3.35 cu124. -| workload | metric | Torch BF16, 3090 | GGUF Q4_K_M, laptop | -|---|---|---:|---:| -| authored144, direct | mean family balanced accuracy | 0.813 | 0.796 | -| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.063 | -| shape777, fresh | decisions / s | 2.33 | 1.40 | -| shape777, serial prefix | decisions / s | 10.75 | 9.21 | -| shape777, parallel shared | decisions / s | 20.03 | 10.88 (8 branches) · 10.51 (auto) | -| shape777, parallel vs fresh | argmax flips / 777 | 6 | 16–18 | +| workload | metric | Torch BF16, 3090 | Torch BF16, laptop 3080 | GGUF Q4_K_M, laptop 3080 | +|---|---|---:|---:|---:| +| authored144, direct | mean family balanced accuracy | 0.813 | 0.813 | 0.796 | +| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.050 | 0.063 | +| shape777, fresh | decisions / s | 2.33 | 1.51 | 1.40 | +| shape777, serial prefix | decisions / s | 10.75 | 10.19 | 9.21 | +| shape777, parallel shared | decisions / s | 20.03 | 14.14 | 10.88 (8 branches) · 10.51 (auto) | +| shape777, parallel vs fresh | argmax flips / 777 | 6 | 3 | 16–18 | + +The laptop BF16 column uses `flash-linear-attention` for the Gated DeltaNet +delta rule; with the reference PyTorch kernels that `pip install -e .` leaves +in place it reads 1.10 / 7.78 / 10.24 decisions per second +(`results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.json`). Raw: `results/raw/llamacpp-gguf-cuda-authored144.json`, `results/raw/calibration/llamacpp-gguf-cuda-authored144.json`, diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 6562fe4..231ffd3 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -28,3 +28,10 @@ fbdf611fb21ee4a01d219d9a321ffe34ed04c264412cbaae89febe62a398a4e9 shape777-llama 4d68ac23eaa09a3f43a16fc3cb212539451d4b17fcc4e221b4f3387a5bcad0f8 shape777-llamacpp-gguf-cuda.predictions.jsonl cc782460f61ffa3ef126b9b702832a2eede6c9cc02a170a76bd059d378e55fca shape777-llamacpp-gguf-cuda-auto.json bf070210ae2b83ce1ebe2f29ab2127eb04620a48d70326df01b872c17c2cc479 shape777-llamacpp-gguf-cuda-auto.predictions.jsonl +882c2d2e809898839c032860952e2a8aea03b53333792577ed58a66e16a5440a predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl +f3143a12fa357cbed8b1e6419ba6ecccf661d40d47dc6da284fe23a17ea041e1 torch-bf16-rtx3080-laptop-authored144.json +8a2a66d5b2d5100f8414147cf63e950c3f677c92336218f0784e94c37d50f28c calibration/torch-bf16-rtx3080-laptop-authored144.json +390b9b3a21e3f1fe0ed9235ab949f90bde07cf6931c9d085bf6c33238a7a9277 shape777-torch-bf16-rtx3080-laptop.json +73c100b8ee0c5b8a9db1330967c1b36ece0198284e1ccd8662b64189e0140234 shape777-torch-bf16-rtx3080-laptop-reference-kernels.json +66e54c5d3434b2e1e84a7064152a73fc7944f54c0428015e27a91d8928079277 shape777-torch-bf16-rtx3080-laptop.predictions.jsonl +27cdbdc70c27f70c1ecf69aca52e7a8b8470ff885dd410e849729e78e087380b shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl diff --git a/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json b/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json new file mode 100644 index 0000000..16fa606 --- /dev/null +++ b/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json @@ -0,0 +1,194 @@ +{ + "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", + "gold": "authored144.jsonl", + "predictions": "torch-bf16-3080-direct-authored144.jsonl", + "rows": 144, + "groups": 36, + "shipped_temperature": 1.2465538333008053, + "cv": { + "folds": 5, + "seed": 217, + "fold_temperatures": [ + 1.3335958912892287, + 1.104457842368847, + 1.1921489937705374, + 1.3375896737108302, + 1.2549894309416632 + ], + "fold_groups": { + "b51b300da2936decc6b7": 0, + "986d7eaba0c7e9738e79": 1, + "770c1607f0a11f88f81e": 2, + "e4cfd694c80e08d3e6a5": 3, + "ca2e0b043985fa49bcd2": 4, + "c979db7416b206c1eb09": 0, + "6dd193172b9240a04bf9": 1, + "a71a17a287f3d30db2aa": 2, + "20460d4ae0db46091e84": 3, + "2b0f8eda8903e38a47de": 4, + "f4bea3d3bcc01056740d": 0, + "f7403e39d6796072491c": 1, + "45f8ead2522a14384cdc": 2, + "6a06a2f278301c1412ee": 3, + "2797f8c23d05c0ee06b3": 4, + "c0cbeb97315307af8111": 0, + "056cc5b726d6e8300a77": 1, + "3ecec8e2c166e397ad07": 2, + "2c3a0cb61f7bae78739e": 3, + "928ebc7817a8f1cac3e4": 4, + "a060fd4621be415e9f1a": 0, + "2fdaa8a61e6e989dd866": 1, + "53b0770ec36835b81d3a": 2, + "e7798dab5f4f121cab77": 3, + "51b91ba31984c5939c1c": 4, + "ffd081ada3bd9be9207a": 0, + "428176cd8ad2c7a74eea": 1, + "d142f7fd0efa0eddc788": 2, + "85051ba3c30277824ebe": 3, + "3476d3f4f32290fe57c3": 4, + "42ed4cbfde7f2f64274b": 0, + "3200eb252d6737e03027": 1, + "389e65bf1ba709b84b12": 2, + "e3ce8ec496dbc9974f94": 3, + "01020a1eaf46ec2fba5d": 4, + "a5b234c2e4fb304f1071": 0 + } + }, + "accuracy_unchanged": 0.8055555555555556, + "ece_uncalibrated": { + "value": 0.05740010907214014, + "ci95": [ + 0.03673802395608913, + 0.1199298494063566 + ] + }, + "ece_calibrated_out_of_fold": { + "value": 0.05016628618424603, + "ci95": [ + 0.037934205532656945, + 0.11432843958410725 + ] + }, + "improvement_ci_separated": false, + "per_family_uncalibrated": { + "candidate_selection": { + "n": 48, + "ece": 0.13479535160707426 + }, + "evidence_interpretation": { + "n": 48, + "ece": 0.0818429607984107 + }, + "rule_application": { + "n": 48, + "ece": 0.045501512952756666 + } + }, + "per_family_calibrated_out_of_fold": { + "candidate_selection": { + "n": 48, + "ece": 0.13038660222444398 + }, + "evidence_interpretation": { + "n": 48, + "ece": 0.04834103813885249 + }, + "rule_application": { + "n": 48, + "ece": 0.049549970892415994 + } + }, + "reliability_bins_uncalibrated": [ + { + "lower": 0.4, + "upper": 0.5, + "n": 6, + "mean_confidence": 0.46722550018127357, + "accuracy": 0.16666666666666666 + }, + { + "lower": 0.5, + "upper": 0.6, + "n": 12, + "mean_confidence": 0.5452229816906935, + "accuracy": 0.5 + }, + { + "lower": 0.6, + "upper": 0.7, + "n": 9, + "mean_confidence": 0.6466211981405114, + "accuracy": 0.5555555555555556 + }, + { + "lower": 0.7, + "upper": 0.8, + "n": 15, + "mean_confidence": 0.7396579630745269, + "accuracy": 0.6666666666666666 + }, + { + "lower": 0.8, + "upper": 0.9, + "n": 16, + "mean_confidence": 0.8474827404278815, + "accuracy": 0.625 + }, + { + "lower": 0.9, + "upper": 1.0, + "n": 86, + "mean_confidence": 0.9819232889393442, + "accuracy": 0.9767441860465116 + } + ], + "reliability_bins_calibrated_out_of_fold": [ + { + "lower": 0.4, + "upper": 0.5, + "n": 9, + "mean_confidence": 0.4583857404798057, + "accuracy": 0.3333333333333333 + }, + { + "lower": 0.5, + "upper": 0.6, + "n": 16, + "mean_confidence": 0.5473707085609272, + "accuracy": 0.4375 + }, + { + "lower": 0.6, + "upper": 0.7, + "n": 13, + "mean_confidence": 0.6627024169930019, + "accuracy": 0.6153846153846154 + }, + { + "lower": 0.7, + "upper": 0.8, + "n": 12, + "mean_confidence": 0.7529320028318748, + "accuracy": 0.5833333333333334 + }, + { + "lower": 0.8, + "upper": 0.9, + "n": 16, + "mean_confidence": 0.8388718597481687, + "accuracy": 0.9375 + }, + { + "lower": 0.9, + "upper": 1.0, + "n": 78, + "mean_confidence": 0.9729208139702882, + "accuracy": 0.9743589743589743 + } + ], + "caveats": [ + "ECE at this n is high-variance; read the bootstrap intervals, not the point value.", + "A single scalar corrects over/under-confidence, not shape miscalibration within a family.", + "shipped_temperature is fit on all labeled rows; the out-of-fold ECE is the honest generalization estimate." + ] +} diff --git a/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl b/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl new file mode 100644 index 0000000..333744e --- /dev/null +++ b/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl @@ -0,0 +1,144 @@ +{"id": "a3f18f3a63d45345942b", "option_ids": ["supported", "insufficient", "contradicted"], "probabilities": [0.010966012159897134, 0.8711382465103751, 0.11789574132972776], "option_logits": [22.0, 26.375, 24.375], "input_tokens": 142, "forward_seconds": 0.6323244820014224, "total_seconds": 0.665138159005437, "prompt_sha256": "3cc9e3d1e4e07885afb7652e9c5f92a5b28168362a3d8e7f6e32e5189a4fccde", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "dtype": 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"Native decisions have no probability estimates; one-hot distributions are not fabricated.", + "Risk/coverage is descriptive; tied confidence values remain together.", + "Analytic event hard-label accuracy measures modal-class agreement, not observed-event accuracy." + ] +} From 5f98fe0513d0de778b9f14a72f71747123bbf598 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 10:45:08 +0200 Subject: [PATCH 6/8] =?UTF-8?q?results:=20the=20line=20the=20PR=20starts?= =?UTF-8?q?=20from=20=E2=80=94=20llama.cpp=20on=20CPU=20as=20merged,=20bef?= =?UTF-8?q?ore=20the=20GPU=20offload?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/LLAMACPP.md | 17 +++++- docs/RESULTS.md | 5 ++ results/raw/SHA256SUMS | 1 + ...hape777-subset5-llamacpp-before-after.json | 60 +++++++++++++++++++ 4 files changed, 82 insertions(+), 1 deletion(-) create mode 100644 results/raw/shape777-subset5-llamacpp-before-after.json diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index dcf4a2a..4ff8dba 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -177,7 +177,22 @@ well-calibrated than BF16; its fitted temperature is nearly the same. | llama.cpp Q4_K_M, RTX 3080 Laptop, `auto` | | | 10.51 | 18 / 777 | Decisions per second; state p50 for the GGUF: 14.97 s fresh, 2.28 s serial, -1.93 s parallel. The two Torch rows on the laptop differ only by the Gated +1.93 s parallel. + +What the table does not show is the line this PR starts from: the backend as +merged in #18 runs on CPU only. Measured on the first 5 states of the fixture +(105 decisions, backend functions timed directly because `shape777.py` takes +only the full fixture; `results/raw/shape777-subset5-llamacpp-before-after.json`): + +| llama.cpp Q4_K_M, same laptop, 5 states | fresh | shared | +|---|---:|---:| +| before: CPU (16 threads), state restore — `#18` as merged | 0.05 | 0.49 | +| after: GPU, state restore (`--llama-parallel 1`) | 1.45 | 9.86 | +| after: GPU, fan-out (`--llama-parallel auto`) | 1.43 | 11.18 | + +The GPU offload is the gain — ×30 fresh, ×20 shared. The fan-out adds 13 % on +this subset and 18 % on the full fixture. The GPU rows of the subset agree with +the full-fixture rows above (9.21 / 10.88), so the subset is representative. The two Torch rows on the laptop differ only by the Gated DeltaNet kernels: `pip install -e .` leaves `transformers` on its reference PyTorch implementation (it says so at load time); with `flash-linear-attention` installed the delta rule runs in Triton. `causal_conv1d` needs `nvcc` and was diff --git a/docs/RESULTS.md b/docs/RESULTS.md index 0f25f6e..0c64c37 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -101,6 +101,11 @@ claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, | shape777, parallel shared | decisions / s | 20.03 | 14.14 | 10.88 (8 branches) · 10.51 (auto) | | shape777, parallel vs fresh | argmax flips / 777 | 6 | 3 | 16–18 | +Before this extension the llama.cpp backend ran on CPU only: 0.05 decisions +per second fresh and 0.49 shared on the same laptop (5-state subset, +`results/raw/shape777-subset5-llamacpp-before-after.json`); the GPU offload is +what turns those into 1.40 and 10.88. + The laptop BF16 column uses `flash-linear-attention` for the Gated DeltaNet delta rule; with the reference PyTorch kernels that `pip install -e .` leaves in place it reads 1.10 / 7.78 / 10.24 decisions per second diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 231ffd3..670417b 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -35,3 +35,4 @@ f3143a12fa357cbed8b1e6419ba6ecccf661d40d47dc6da284fe23a17ea041e1 torch-bf16-rtx 73c100b8ee0c5b8a9db1330967c1b36ece0198284e1ccd8662b64189e0140234 shape777-torch-bf16-rtx3080-laptop-reference-kernels.json 66e54c5d3434b2e1e84a7064152a73fc7944f54c0428015e27a91d8928079277 shape777-torch-bf16-rtx3080-laptop.predictions.jsonl 27cdbdc70c27f70c1ecf69aca52e7a8b8470ff885dd410e849729e78e087380b shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl +681e1f4257d328974780a06ea4c82efee8b6510d15cba09572f673e691a5afe7 shape777-subset5-llamacpp-before-after.json diff --git a/results/raw/shape777-subset5-llamacpp-before-after.json b/results/raw/shape777-subset5-llamacpp-before-after.json new file mode 100644 index 0000000..53f68a1 --- /dev/null +++ b/results/raw/shape777-subset5-llamacpp-before-after.json @@ -0,0 +1,60 @@ +{ + "fixture": "benchmarks/data/shape777.jsonl, first 5 states (105 decisions); fresh timed on the first state only", + "method": "semif_phase1.llamacpp_backend.score / score_shared timed directly (shape777.py only accepts the full fixture); warm model, wall time, output write excluded", + "gguf": "bartowski/Qwen_Qwen3.5-4B-GGUF Q4_K_M", + "cpu": "Intel Xeon W-11955M, 16 threads, llama-cpp-python 0.3.35 PyPI wheel", + "gpu": "RTX 3080 Laptop 16 GB, cu124 wheel", + "runs": { + "before_pr_cpu_state_restore": { + "mode": "cpu", + "states": 5, + "gpu_offload": false, + "n_gpu_layers": 0, + "fresh": { + "decisions": 21, + "seconds": 457.86226421000174, + "per_second": 0.04586532160765318 + }, + "shared": { + "decisions": 105, + "seconds": 213.48030593199655, + "per_second": 0.49184864871538736, + "state_seconds": 42.69606118639931 + } + }, + "after_pr_gpu_state_restore": { + "mode": "gpu-restore", + "states": 5, + "gpu_offload": true, + "n_gpu_layers": -1, + "fresh": { + "decisions": 21, + "seconds": 14.507362655000179, + "per_second": 1.447540845252258 + }, + "shared": { + "decisions": 105, + "seconds": 10.65108915699966, + "per_second": 9.858146754033724, + "state_seconds": 2.130217831399932 + } + }, + "after_pr_gpu_fanout": { + "mode": "gpu-fanout", + "states": 5, + "gpu_offload": true, + "n_gpu_layers": -1, + "fresh": { + "decisions": 21, + "seconds": 14.73041090300103, + "per_second": 1.4256221457964668 + }, + "shared": { + "decisions": 105, + "seconds": 9.390513983998972, + "per_second": 11.181496580369876, + "state_seconds": 1.8781027967997943 + } + } + } +} \ No newline at end of file From b7a1ffb3d6dc241907f22a6ec3af04b5de18161f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 11:12:14 +0200 Subject: [PATCH 7/8] =?UTF-8?q?llamacpp:=20drop=20the=20marginal=20readout?= =?UTF-8?q?=20=E2=80=94=20on=20Qwen3-8B=20under=20this=20prompt=20it=20has?= =?UTF-8?q?=20nothing=20to=20fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/LLAMACPP.md | 29 +------- src/semif_phase1/cli.py | 10 +-- src/semif_phase1/llamacpp_backend.py | 104 +++------------------------ tests/test_llamacpp.py | 56 +-------------- 4 files changed, 15 insertions(+), 184 deletions(-) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 4ff8dba..e93be99 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -106,9 +106,8 @@ build option; the same wheel serves both modes. `tests/test_llamacpp.py::test_real_gguf_scores_direct_serial_and_shared` loads the real GGUF twice — with one sequence and with three — and checks that direct, -serial, restore-shared, copy-shared and marginal readouts agree on the -decisions, that copy-shared option logits match restore-shared ones within -0.5, and that `preamble_mass` stays in `[0, 1]`. Set `SEMIF_LLAMACPP_GGUF` to run +serial, restore-shared and copy-shared readouts agree on the decisions and +that copy-shared option logits match restore-shared ones within 0.5. Set `SEMIF_LLAMACPP_GGUF` to run it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: - the PyPI CPU wheel of `llama-cpp-python` 0.3.35, CPU only; @@ -120,30 +119,6 @@ it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: or AMX) for any decode, single- or multi-sequence, hybrid or pure-attention model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. -## Marginal readout - -*Validated for correctness, not for a metric gain.* On Qwen3.5-4B, which answers with the letter -directly, every result in this document is identical with or without it, and no committed workload -needs it. It is here for checkpoints that put their first-token mass on a preamble token; the case -that motivated it — Qwen3-8B answering `**` before the letter, 60 % of the mass leaking without it, -10 % with a marginalised read — was measured outside this repository, on a mail-triage fixture that -cannot be redistributed. - - -`--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how -the answer is read, not what is asked. Some checkpoints want to emit a token -*before* the letter — Qwen3-8B puts almost all of its next-token mass on the -Markdown `**` token when the assistant turn starts empty, and reading only the -last prompt position then reads that preamble instead of the answer. The -marginal readout takes, for each branch, the most likely non-slot tokens above -a floor (two probes, 5 %), appends each in one extra batched decode across all -branches, reads the slot masses after it, and adds them to the direct slot -masses weighted by the preamble's probability. `option_logits` then hold the -natural logs of the summed masses, so `softmax` and temperature scaling keep -working unchanged; `preamble_mass` records the share that came through a -preamble. On Qwen3.5-4B, which answers with the letter directly, the readout -changes nothing and `preamble_mass` stays at zero. - ## Results RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` 0.3.35 cu124. diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index 303a22f..7264240 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -30,9 +30,6 @@ def main() -> None: help="Shared-mode branching for --backend llamacpp: 'auto' sizes each fan-out from the " "state's token counts over a unified KV buffer; N fixes n_seq_max; 1 restores " "state per decision") - parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", - help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " - "over one-token preambles (needs --llama-parallel > 1)") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -77,15 +74,11 @@ def _auto_or_int(name, value, minimum): for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): if args.backend != "llamacpp" and value != "auto": parser.error(f"{name} requires --backend llamacpp") - if args.llama_readout != "last" and args.backend != "llamacpp": - parser.error("--llama-readout requires --backend llamacpp") if args.backend == "llamacpp": if args.mode == "reranker": parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel == 1): - parser.error("--llama-readout marginal requires --mode shared and --llama-parallel other than 1") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") @@ -106,8 +99,7 @@ def _auto_or_int(name, value, minimum): model, tokenizer, metadata = llamacpp_backend.load_model( args.model, args.revision, args.gguf, threads=args.llama_threads, context_tokens=args.max_tokens, - gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel, - readout=args.llama_readout) + gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel) direct, serial, shared = (llamacpp_backend.score, llamacpp_backend.SerialPrefixScorer, llamacpp_backend.score_shared) else: diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 9b366cc..7422db2 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -19,8 +19,7 @@ cannot be partially erased, so ``llama_memory_seq_rm`` refuses to truncate a tail back to the prefix. They do support whole-sequence removal, state save/restore, and sequence copies (``llama_memory_recurrent::seq_cp``), which is -what both branching modes rely on. Pure-attention models additionally allow -tail truncation, which the optional marginal readout uses when available. +what both branching modes rely on. """ from __future__ import annotations @@ -41,7 +40,6 @@ from .shared import _state_prefix DECODE_CHUNK = 512 -READOUTS = ("last", "marginal") AUTO = "auto" #: Upper bound on branches per fan-out when `sequences` is "auto". With a unified KV buffer the #: bound costs bookkeeping only; the real limit is the token budget, checked per state. @@ -268,16 +266,6 @@ def fanout_logits(self, prefix_length: int, suffixes: list[list[int]]) -> list[n for branch in branches: self.lib.llama_memory_seq_rm(self.memory, branch, -1, -1) - def probe_branches(self, positions: dict[int, int], tokens: dict[int, int]) -> dict[int, numpy.ndarray]: - """Append one token to each listed branch and read the logits after it. - - Used by the marginal readout: ``tokens[branch]`` is the preamble token the model wanted - to emit first, ``positions[branch]`` the position it goes to. Branches are live sequences - from the current fan-out; the caller removes them. - """ - items = [(tokens[branch], positions[branch], branch, True) for branch in sorted(tokens)] - return self._decode_items(items) - def _free_native(engine: _Engine, library, model) -> None: engine.close() @@ -323,7 +311,7 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, context_tokens: int = 4096, gpu_layers: int | str | None = AUTO, - sequences: int | str = AUTO, readout: str = "last"): + sequences: int | str = AUTO): """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. ``gpu_layers``: ``"auto"`` (or ``None``) leaves llama.cpp's default, which offloads every @@ -356,8 +344,6 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, sequences = 1 + BRANCH_CAP if not (isinstance(sequences, int) and sequences >= 1): raise ValueError("sequences must be 'auto' or a positive integer") - if readout not in READOUTS: - raise ValueError(f"readout must be one of {READOUTS}") import transformers try: import llama_cpp @@ -403,7 +389,6 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "n_seq_max": sequences, "branch_sizing": "auto" if auto_sequences else "fixed", "kv_unified": engine.kv_unified, - "readout_mode": readout, "max_prompt_tokens": context_tokens, "context_tokens": engine.context_tokens, "decode_chunk": DECODE_CHUNK, @@ -523,60 +508,6 @@ def _chunks(prefix_length: int, suffix_lengths: list[int], budget: int, cap: int return chunks -def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, - suffixes: list[list[int]], vocabularies: list[numpy.ndarray], - slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): - """Fold one-token preambles back into the answer-slot masses. - - Some checkpoints want to emit a token before the letter — Qwen3-8B puts almost all of its - mass on the Markdown ``**`` token when the assistant turn starts empty. Reading only the last - prompt position then reads the preamble, not the answer. For each branch whose most likely - next tokens are not answer slots, this appends up to ``probes`` such tokens (one extra batched - decode per probe rank across all branches), reads the slot masses after them, and adds them - to the direct slot masses weighted by the preamble's probability. The argmax and the reported - ``option_logits`` become log-masses of that truncated path sum. - - Returns per-branch (option_logits, preamble_mass) with option_logits as natural logs of the - summed masses, so downstream softmax and temperature scaling keep working unchanged. - """ - live = {branch: prefix_length + len(suffix) for branch, suffix in zip(branches, suffixes)} - masses, via = [], [] - for vocabulary, slots in zip(vocabularies, slot_lists): - probabilities = _stable_softmax(vocabulary.astype(numpy.float64)) - masses.append(probabilities[slots].copy()) - via.append(0.0) - # Candidate preambles per branch: top tokens that are not answer slots. - candidates: list[list[int]] = [] - for vocabulary, slots in zip(vocabularies, slot_lists): - forbidden = set(slots) - order = numpy.argsort(-vocabulary)[: probes + len(slots)] - candidates.append([int(token) for token in order if int(token) not in forbidden][:probes]) - weights = [_stable_softmax(vocabulary.astype(numpy.float64)) for vocabulary in vocabularies] - for rank in range(probes): - tokens, positions = {}, {} - for index, branch in enumerate(branches): - if rank < len(candidates[index]): - token = candidates[index][rank] - if weights[index][token] >= floor: - tokens[branch] = token - positions[branch] = live[branch] - if not tokens: - break - after = engine.probe_branches(positions, tokens) - for index, branch in enumerate(branches): - if branch in tokens: - weight = float(weights[index][tokens[branch]]) - gain = weight * _stable_softmax(after[branch].astype(numpy.float64))[slot_lists[index]] - masses[index] += gain - via[index] += float(gain.sum()) - live[branch] += 1 - results = [] - for mass, mass_via in zip(masses, via): - total = float(mass.sum()) - results.append((numpy.log(numpy.maximum(mass, 1e-300)).tolist(), mass_via / total if total > 0 else 0.0)) - return results - - def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): """Prefill one exact state once, then score every criterion from branches. @@ -594,7 +525,6 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: raise ValueError("The fixed state prefix does not match every full prompt") encode_seconds = time.perf_counter() - started engine = model.engine - marginal = metadata.get("readout_mode") == "marginal" mark = time.perf_counter() engine.clear() engine.prefill(prefix) @@ -604,8 +534,6 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: copy_seconds = suffix_seconds = 0.0 results = [] if engine.sequences == 1: - if marginal: - raise ValueError("The marginal readout needs sequence copies; load with sequences > 1") for row, row_encoded in zip(rows, encoded): ids, slots, _ = row_encoded mark = time.perf_counter() @@ -648,34 +576,20 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: ] logits = engine._decode_items(items) vocabularies = [logits[branch] for branch in branches] - if marginal: - folded = _marginal_readout(engine, branches, len(prefix), suffixes, - vocabularies, slot_lists) finally: for branch in branches: engine.lib.llama_memory_seq_rm(engine.memory, branch, -1, -1) suffix_seconds += time.perf_counter() - mark for index, (row, row_encoded) in enumerate(zip(chunk_rows, chunk_encoded)): vocabulary = vocabularies[index] - if marginal: - selected, preamble_mass = folded[index] - result = _result( - row, row_encoded, selected, vocabulary, metadata, - "llamacpp-seq-copy-parallel-marginal-v1", - "quantized branch logits over copied prefix sequences, answer-slot masses " - "summed over one-token preambles; option_logits are log-masses", - ) - result["preamble_mass"] = preamble_mass - else: - result = _result( - row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, - "llamacpp-seq-copy-parallel-v1", - "quantized branch last-position logits over copied prefix sequences; " - "one batched decode per chunk", - ) - results.append(result) + results.append(_result( + row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, + "llamacpp-seq-copy-parallel-v1", + "quantized branch last-position logits over copied prefix sequences; " + "one batched decode per chunk", + )) branch_bytes = 0 - config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" + config = "llamacpp-seq-copy-parallel-v1" if engine.sequences == 1: branch_counts = [1] * len(rows) suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 05e328a..c98314c 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -20,7 +20,6 @@ (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-parallel", "0"], "at least 1"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-gpu-layers", "many"], "must be 'auto' or an integer"), - (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): monkeypatch.setattr(sys, "argv", ["semif-score", "--model", "unused", "--revision", "unused", @@ -36,9 +35,9 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): import semif_phase1 fake_backend = SimpleNamespace( - load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences, readout: + load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences: (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens, - "gpu_layers": gpu_layers, "sequences": sequences, "readout": readout}), + "gpu_layers": gpu_layers, "sequences": sequences}), score=lambda model, tokenizer, row, metadata, max_tokens: metadata, SerialPrefixScorer=None, score_shared=None, ) @@ -56,7 +55,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 - assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" and record["readout"] == "last" + assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" def test_load_model_rejects_unpinned_remote_revision(): @@ -249,11 +248,6 @@ def test_real_gguf_scores_direct_serial_and_shared(): for shared_result, fanned_result in zip(shared, fanned): numpy.testing.assert_allclose( shared_result["option_logits"], fanned_result["option_logits"], rtol=0, atol=0.5) - marginal_meta = {**metadata, "readout_mode": "marginal"} - folded, timing = llamacpp_backend.score_shared(model, tokenizer, rows, marginal_meta) - assert timing["serving_config"] == "llamacpp-seq-copy-parallel-marginal-v1" - assert choices(folded) == ["yes", "yes"] - assert all(0.0 <= result["preamble_mass"] <= 1.0 for result in folded) finally: model.close() @@ -268,8 +262,6 @@ def test_load_model_validates_new_arguments(tmp_path): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-2) with pytest.raises(ValueError, match="sequences"): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) - with pytest.raises(ValueError, match="readout"): - llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, readout="first") def _fake_engine_for_fanout(sequences: int, vocab_size: int = 8): @@ -343,48 +335,6 @@ def test_fanout_refuses_more_branches_than_slots(): engine.fanout_logits(3, [[1], [2]]) -def test_marginal_readout_folds_preamble_mass_into_slots(): - from semif_phase1.llamacpp_backend import _marginal_readout - - probed = {} - - class Engine: - def probe_branches(self, positions, tokens): - probed.update(tokens) - # After the preamble token the model answers slot 1 (token id 1) with certainty. - out = numpy.full(8, -20.0) - out[1] = 20.0 - return {branch: out.copy() for branch in tokens} - - # Branch 1: the model puts its mass on token 7 (a preamble), almost none on the slots 0 and 1. - vocabulary = numpy.full(8, -20.0) - vocabulary[7] = 20.0 - vocabulary[0] = 1.0 - vocabulary[1] = 0.0 - (logits, preamble_mass), = _marginal_readout( - Engine(), branches=[1], prefix_length=10, suffixes=[[4, 4]], - vocabularies=[vocabulary], slot_lists=[[0, 1]]) - assert probed == {1: 7} - # Direct read alone would have picked slot 0; the preamble path moves the answer to slot 1. - assert int(numpy.argmax(logits)) == 1 - assert preamble_mass > 0.99 - - -def test_marginal_readout_skips_branches_that_already_answer_with_a_slot(): - from semif_phase1.llamacpp_backend import _marginal_readout - - class Engine: - def probe_branches(self, positions, tokens): - raise AssertionError("no probe should be issued") - - vocabulary = numpy.full(8, -20.0) - vocabulary[0] = 20.0 # slot 0 is the confident next token; nothing else clears the floor - (logits, preamble_mass), = _marginal_readout( - Engine(), branches=[1], prefix_length=3, suffixes=[[9]], - vocabularies=[vocabulary], slot_lists=[[0, 1]]) - assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 - - def test_chunks_follow_the_token_budget_and_the_cap(): from semif_phase1.llamacpp_backend import _chunks From bf5ded8175e8c5b5528a8f69fafeafa9f0688f78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 08:39:32 +0200 Subject: [PATCH 8/8] Make the direct prompt pluggable: built-in en/fr wordings, prompt files, prompt_version per wording --- README.md | 3 + docs/PROMPTS.md | 76 +++++++++++++++++++ src/semif_phase1/cli.py | 18 ++++- src/semif_phase1/core.py | 109 +++++++++++++++++++++++---- src/semif_phase1/direct.py | 15 ++-- src/semif_phase1/llamacpp_backend.py | 46 +++++------ src/semif_phase1/mlx_backend.py | 33 ++++---- src/semif_phase1/serial.py | 26 ++++--- src/semif_phase1/shared.py | 26 ++++--- tests/test_cli.py | 7 +- tests/test_llamacpp.py | 6 +- tests/test_prompt.py | 90 ++++++++++++++++++++++ tests/test_shared.py | 4 +- 13 files changed, 366 insertions(+), 93 deletions(-) create mode 100644 docs/PROMPTS.md create mode 100644 tests/test_prompt.py diff --git a/README.md b/README.md index 44c24f6..b730fc1 100644 --- a/README.md +++ b/README.md @@ -180,6 +180,8 @@ Calibration does not change the selected option. The clear improvement is on WAN Returned probabilities are conditional on the supplied options. Calibrate and validate them on the workload where they will make decisions. `state` may also be a nonempty JSON object or array. Direct modes preserve it as structured JSON; reranker mode renders it as document text. +The words around the input — the system instruction and the JSON key names — come from a prompt: `--prompt en` (default, the published wording), `--prompt fr`, or a JSON file. The payload shape and the answer letters never change; `prompt_version` in every result names the wording. See [docs/PROMPTS.md](docs/PROMPTS.md). + ## Documentation - [Results](docs/RESULTS.md) — quality, speed, perturbations, and claim boundaries @@ -187,6 +189,7 @@ Returned probabilities are conditional on the supplied options. Calibrate and va - [Reproduce](docs/REPRODUCE.md) — exact environment, pinned commands, perturbations, and verification - [Apple Silicon](docs/APPLE_SILICON.md) — MPS and optional MLX backends - [Calibration](docs/CALIBRATION.md) — fitted temperatures, out-of-fold evidence, and application +- [Prompts](docs/PROMPTS.md) — the pluggable prompt: built-in wordings, prompt files, what never changes - [EXL3 bridge](exl3-bridge/README.md) — quantized 27B runner and committed evidence - [Interactive replay](demo/index.html) - [Browser-only WebGPU demo](webgpu-demo/index.html) — no waitlist; use it today diff --git a/docs/PROMPTS.md b/docs/PROMPTS.md new file mode 100644 index 0000000..4dbc7a0 --- /dev/null +++ b/docs/PROMPTS.md @@ -0,0 +1,76 @@ +# Prompts + +Every direct-mode result carries `prompt_version` and `prompt_sha256`. Until now there was one +prompt, `direct-options-v1`, written in English with English JSON keys, and the published numbers +all use it. This page describes the pluggable prompt behind `--prompt`: what a prompt may change, +what it may not, and how to add one. + +## What a prompt is + +A `Prompt` (`semif_phase1.core.Prompt`) is the *words* of the direct prompt: the system instruction +and the five key names of the user payload. The *shape* of the payload is fixed: + +```json +{"evidence": , "criterion": , "options": [{"letter": "A", "description": "..."}, ...]} +``` + +Evidence first, then the criterion, then the lettered options. The serial and shared modes depend +on that order: they cut the reusable prefix at the end of the evidence, so a state can be prefilled +once and every criterion scored from it. A prompt renames the keys and rewrites the instruction; it +cannot reorder the payload, add fields, or change the answer slots, which stay the uppercase +letters `A`–`P` whatever the language. + +## Built-in prompts + +| name | `prompt_version` | system instruction | keys | +|---|---|---|---| +| `en` (default) | `direct-options-v1` | *Apply the supplied criterion to the supplied evidence. Choose exactly one listed option. Respond with only its uppercase letter, with no explanation or reasoning.* | `evidence`, `criterion`, `options`, `letter`, `description` | +| `fr` | `direct-options-fr-v1` | *Applique le critère fourni aux éléments fournis. Choisis exactement une option de la liste. Réponds uniquement par sa lettre majuscule, sans explication ni raisonnement.* | `elements`, `critere`, `options`, `lettre`, `description` | + +`--prompt en` renders byte for byte what the code rendered before this option existed: +`tests/test_prompt.py` pins it, and the published `prompt_sha256` values still verify. + +## A prompt from a file + +```bash +semif-score --mode shared --backend llamacpp --prompt prompts/de.json ... +``` + +```json +{ + "version": "direct-options-de-v1", + "system": "Wende das angegebene Kriterium auf die angegebenen Belege an. Wähle genau eine der aufgeführten Optionen. Antworte nur mit ihrem Großbuchstaben, ohne Erklärung oder Begründung.", + "keys": {"evidence": "belege", "criterion": "kriterium", "options": "optionen", "letter": "buchstabe", "description": "beschreibung"} +} +``` + +`keys` and each of its entries are optional and default to the English names. The loader refuses a +file without `version` or `system`, a key it does not know, two keys with the same name, and a +`version` that belongs to a built-in prompt with different words — `prompt_version` in a result +must always mean one wording. + +## What changes with the prompt, and what to expect + +Only the words change, but option scores are conditional on the words: a different prompt is a +different measurement, with its own `prompt_version`, and numbers from two prompts are not +comparable without saying so. The letter slots are verified for every prompt exactly as before +(single round-trip tokens, boundary check, GGUF agreement). + +Whether a model does better when addressed in the evidence's language is a question to measure on +the workload, not to assume. The one measurement made while adding this option, on a private +French-language e-mail set of 300 messages and four yes/no criteria with the pinned Qwen3.5-4B +GGUF, is reported in the pull request that introduced it; it is not part of this repository's +published results. + +## In code + +```python +from semif_phase1.core import resolve_prompt +from semif_phase1 import llamacpp_backend + +prompt = resolve_prompt("fr") # or a Prompt(...), or a path +results, timing = llamacpp_backend.score_shared(model, tokenizer, rows, metadata, prompt=prompt) +``` + +`score`, `SerialPrefixScorer`, `score_shared` and `_state_prefix` in every backend accept +`prompt=`; leaving it out means `en`. diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index 7264240..a3a5330 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -4,9 +4,10 @@ import argparse import json +from functools import partial from pathlib import Path -from .core import load_causal_model, validate_row +from .core import load_causal_model, resolve_prompt, validate_row from .direct import score as direct_score from .reranker import score as reranker_score from .serial import SerialPrefixScorer @@ -30,6 +31,9 @@ def main() -> None: help="Shared-mode branching for --backend llamacpp: 'auto' sizes each fan-out from the " "state's token counts over a unified KV buffer; N fixes n_seq_max; 1 restores " "state per decision") + parser.add_argument("--prompt", default="en", + help="Wording of the direct prompt: 'en' (default, the published prompt), 'fr', or a JSON " + "file (docs/PROMPTS.md). Any other wording changes prompt_version and prompt_sha256") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -42,6 +46,12 @@ def main() -> None: args = parser.parse_args() if args.output.exists() or args.max_tokens < 1: parser.error("Output must be new and max-tokens must be positive") + if args.mode == "reranker" and args.prompt != "en": + parser.error("--prompt applies to direct, serial, and shared modes; the reranker has its own prompt") + try: + prompt = resolve_prompt(args.prompt) + except ValueError as error: + parser.error(str(error)) if args.mlx_bits and args.backend != "mlx": parser.error("--mlx-bits requires --backend mlx") if args.mlx_cache_limit_mib is not None: @@ -112,16 +122,16 @@ def _auto_or_int(name, value, minimum): args.output.parent.mkdir(parents=True, exist_ok=True) with args.output.open("x") as destination: if args.mode == "shared": - results, timing = shared(model, tokenizer, rows, metadata, args.max_tokens) + results, timing = shared(model, tokenizer, rows, metadata, args.max_tokens, prompt=prompt) for result in results: destination.write(json.dumps({**result, "shared_timing": timing}, allow_nan=False) + "\n") elif args.mode == "serial": - scorer = serial(model, tokenizer, metadata, args.max_tokens) + scorer = serial(model, tokenizer, metadata, args.max_tokens, prompt=prompt) for row in rows: destination.write(json.dumps(scorer.score(row), allow_nan=False) + "\n") destination.flush() else: - scorer = direct if args.mode == "direct" else reranker_score + scorer = partial(direct, prompt=prompt) if args.mode == "direct" else reranker_score for row in rows: destination.write(json.dumps(scorer(model, tokenizer, row, metadata, args.max_tokens), allow_nan=False) + "\n") destination.flush() diff --git a/src/semif_phase1/core.py b/src/semif_phase1/core.py index 57e8610..bf7ec9f 100644 --- a/src/semif_phase1/core.py +++ b/src/semif_phase1/core.py @@ -2,6 +2,7 @@ from __future__ import annotations +import dataclasses import hashlib import json import math @@ -15,6 +16,98 @@ ) +@dataclasses.dataclass(frozen=True) +class Prompt: + """The words of one direct-decision prompt: its system instruction and payload key names. + + The payload shape never changes — evidence first, then the criterion, then the lettered + options — because the serial and shared modes cut the reusable state prefix at the end of the + evidence. A prompt only changes the words around that shape, so a model can be addressed in + the language of its evidence. Every prompt carries its own ``version``, written into each + result next to ``prompt_sha256``; the published numbers all use ``direct-options-v1``. + """ + + version: str + system: str + evidence_key: str = "evidence" + criterion_key: str = "criterion" + options_key: str = "options" + letter_key: str = "letter" + description_key: str = "description" + + def __post_init__(self) -> None: + if not all(isinstance(value, str) and value.strip() for value in (self.version, self.system)): + raise ValueError("A prompt needs a nonempty version and system instruction") + keys = self.keys() + if len(set(keys)) != len(keys) or not all(isinstance(key, str) and key.strip() for key in keys): + raise ValueError("Prompt payload keys must be distinct nonempty strings") + + def keys(self) -> tuple[str, ...]: + return (self.evidence_key, self.criterion_key, self.options_key, self.letter_key, self.description_key) + + def messages(self, row: dict) -> list[dict]: + validate_row(row) + payload = { + self.evidence_key: row["state"], + self.criterion_key: row["question"], + self.options_key: [ + {self.letter_key: LETTERS[index], self.description_key: option["description"]} + for index, option in enumerate(row["options"]) + ], + } + return [ + {"role": "system", "content": self.system}, + {"role": "user", "content": json.dumps(payload, ensure_ascii=False)}, + ] + + def evidence_text(self, state) -> str: + """The user payload up to the end of the evidence: the part every decision on a state shares.""" + return json.dumps({self.evidence_key: state}, ensure_ascii=False)[:-1] + + +DEFAULT_PROMPT = Prompt("direct-options-v1", DIRECT_SYSTEM) +PROMPTS = { + "en": DEFAULT_PROMPT, + "fr": Prompt( + "direct-options-fr-v1", + "Applique le critère fourni aux éléments fournis. Choisis exactement une option de la liste. " + "Réponds uniquement par sa lettre majuscule, sans explication ni raisonnement.", + evidence_key="elements", criterion_key="critere", options_key="options", + letter_key="lettre", description_key="description", + ), +} + + +def resolve_prompt(spec=None) -> Prompt: + """A ``Prompt`` from its name (``en``, ``fr``), a JSON file, an existing ``Prompt``, or ``None``. + + A file holds ``{"version": ..., "system": ..., "keys": {"evidence": ..., "criterion": ..., + "options": ..., "letter": ..., "description": ...}}``; ``keys`` and each of its entries are + optional and default to the English names. A custom prompt may not reuse a built-in version + string unless it is that prompt, so ``prompt_version`` in a result always means one wording. + """ + if spec is None: + return DEFAULT_PROMPT + if isinstance(spec, Prompt): + return spec + if isinstance(spec, str) and spec in PROMPTS: + return PROMPTS[spec] + path = Path(spec) + if not path.is_file(): + raise ValueError(f"Unknown prompt {spec!r}: expected one of {sorted(PROMPTS)} or a JSON file") + data = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(data, dict) or not {"version", "system"} <= data.keys(): + raise ValueError(f"{path}: a prompt file needs 'version' and 'system'") + keys = data.get("keys") or {} + if not isinstance(keys, dict) or not keys.keys() <= {"evidence", "criterion", "options", "letter", "description"}: + raise ValueError(f"{path}: 'keys' may only rename evidence, criterion, options, letter, description") + prompt = Prompt(data["version"], data["system"], **{f"{name}_key": value for name, value in keys.items()}) + for built_in in PROMPTS.values(): + if prompt.version == built_in.version and prompt != built_in: + raise ValueError(f"{path}: version {prompt.version!r} belongs to a built-in prompt with other words") + return prompt + + def validate_row(row: dict) -> None: required = {"id", "state", "question", "options"} if not required <= row.keys(): @@ -40,20 +133,8 @@ def validate_row(row: dict) -> None: raise ValueError("Option IDs must be unique") -def direct_messages(row: dict) -> list[dict]: - validate_row(row) - payload = { - "evidence": row["state"], - "criterion": row["question"], - "options": [ - {"letter": LETTERS[index], "description": option["description"]} - for index, option in enumerate(row["options"]) - ], - } - return [ - {"role": "system", "content": DIRECT_SYSTEM}, - {"role": "user", "content": json.dumps(payload, ensure_ascii=False)}, - ] +def direct_messages(row: dict, prompt: Prompt | None = None) -> list[dict]: + return resolve_prompt(prompt).messages(row) def softmax(values: list[float]) -> list[float]: diff --git a/src/semif_phase1/direct.py b/src/semif_phase1/direct.py index 930eb49..187fef7 100644 --- a/src/semif_phase1/direct.py +++ b/src/semif_phase1/direct.py @@ -5,9 +5,9 @@ import inspect import time -from .core import LETTERS, digest, direct_messages, softmax, synchronize +from .core import DEFAULT_PROMPT, LETTERS, digest, resolve_prompt, softmax, synchronize -PROMPT_VERSION = "direct-options-v1" +PROMPT_VERSION = DEFAULT_PROMPT.version def _slot_ids(tokenizer, count: int) -> list[int]: @@ -30,10 +30,10 @@ def _forward(model, inputs): return model(**kwargs).logits[:, -1, :] -def encode_prompt(tokenizer, row: dict, max_tokens: int) -> tuple[list[int], list[int], str]: +def encode_prompt(tokenizer, row: dict, max_tokens: int, prompt=None) -> tuple[list[int], list[int], str]: """Encode one decision and verify its single-token answer slots.""" prompt = tokenizer.apply_chat_template( - direct_messages(row), tokenize=False, add_generation_prompt=True, enable_thinking=False + resolve_prompt(prompt).messages(row), tokenize=False, add_generation_prompt=True, enable_thinking=False ) ids = tokenizer.encode(prompt, add_special_tokens=False) if not ids or len(ids) > max_tokens: @@ -45,11 +45,12 @@ def encode_prompt(tokenizer, row: dict, max_tokens: int) -> tuple[list[int], lis return ids, slots, digest(prompt) -def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) -> dict: +def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096, prompt=None) -> dict: import torch + prompt = resolve_prompt(prompt) started = time.perf_counter() - ids, slots, prompt_hash = encode_prompt(tokenizer, row, max_tokens) + ids, slots, prompt_hash = encode_prompt(tokenizer, row, max_tokens, prompt) device = next(model.parameters()).device inputs = { "input_ids": torch.tensor([ids], dtype=torch.long, device=device), @@ -70,7 +71,7 @@ def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) - "forward_seconds": time.perf_counter() - forward_start, "total_seconds": time.perf_counter() - started, "prompt_sha256": prompt_hash, - "prompt_version": PROMPT_VERSION, + "prompt_version": prompt.version, "model": metadata, "readout": "native full-vocabulary last-position logits restricted to declared answer slots", "probability_status": "conditional option score; uncalibrated as decision confidence", diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 7422db2..59df58e 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -35,8 +35,8 @@ import numpy -from .core import LETTERS, direct_messages, softmax -from .direct import PROMPT_VERSION, encode_prompt +from .core import LETTERS, resolve_prompt, softmax +from .direct import encode_prompt from .shared import _state_prefix DECODE_CHUNK = 512 @@ -64,9 +64,9 @@ def _model_params(library, gpu_layers: int | None): return params -def _render(tokenizer, row: dict) -> str: +def _render(tokenizer, row: dict, prompt=None) -> str: return tokenizer.apply_chat_template( - direct_messages(row), tokenize=False, add_generation_prompt=True, enable_thinking=False + resolve_prompt(prompt).messages(row), tokenize=False, add_generation_prompt=True, enable_thinking=False ) @@ -284,9 +284,9 @@ def __init__(self, engine: _Engine, model, vocab, tokenizer): def close(self) -> None: self._finalizer() - def encode_verified(self, row: dict, max_tokens: int): - ids, slots, prompt_hash = encode_prompt(self.tokenizer, row, max_tokens) - if _gguf_tokenize(self.engine.lib, self.vocab, _render(self.tokenizer, row)) != ids: + def encode_verified(self, row: dict, max_tokens: int, prompt=None): + ids, slots, prompt_hash = encode_prompt(self.tokenizer, row, max_tokens, prompt) + if _gguf_tokenize(self.engine.lib, self.vocab, _render(self.tokenizer, row, prompt)) != ids: raise ValueError(f"Row {row['id']}: GGUF tokenization disagrees with the reference tokenizer") return ids, slots, prompt_hash @@ -398,7 +398,8 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, return _Backend(engine, model, vocab, tokenizer), tokenizer, metadata -def _result(row: dict, encoded, selected: list[float], vocabulary, metadata: dict, config: str, readout: str) -> dict: +def _result(row: dict, encoded, selected: list[float], vocabulary, metadata: dict, config: str, readout: str, + prompt) -> dict: ids, slots, prompt_hash = encoded return { "id": row["id"], @@ -410,23 +411,24 @@ def _result(row: dict, encoded, selected: list[float], vocabulary, metadata: dic "allowed_token_mass": float(numpy.exp(_logsumexp(numpy.asarray(selected)) - _logsumexp(vocabulary))), "full_vocab_argmax_id": int(vocabulary.argmax()), "prompt_sha256": prompt_hash, - "prompt_version": PROMPT_VERSION, + "prompt_version": prompt.version, "model": {**metadata, "serving_config": config}, "readout": readout, "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence", } -def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) -> dict: +def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096, prompt=None) -> dict: + prompt = resolve_prompt(prompt) started = time.perf_counter() - encoded = model.encode_verified(row, max_tokens) + encoded = model.encode_verified(row, max_tokens, prompt) mark = time.perf_counter() vocabulary = model.engine.full_logits(encoded[0]) selected = vocabulary[encoded[1]].tolist() result = _result( row, encoded, selected, vocabulary, metadata, "llamacpp-direct-v1", "quantized last-position logits restricted to declared answer slots; no generated tokens", - ) + prompt) result.update(forward_seconds=time.perf_counter() - mark, total_seconds=time.perf_counter() - started) return result @@ -434,9 +436,10 @@ def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) - class SerialPrefixScorer: """Cache the current state once, then score restored-state branch suffixes.""" - def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096): + def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096, prompt=None): self.model = model self.tokenizer = tokenizer + self.prompt = resolve_prompt(prompt) self.metadata = {**metadata, "serving_config": "llamacpp-state-restore-v1"} self.max_tokens = max_tokens self.prefix = None @@ -444,9 +447,9 @@ def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096): def score(self, row: dict) -> dict: started = time.perf_counter() - encoded = self.model.encode_verified(row, self.max_tokens) + encoded = self.model.encode_verified(row, self.max_tokens, self.prompt) ids, slots, _ = encoded - prefix = _state_prefix(self.tokenizer, row["state"]) + prefix = _state_prefix(self.tokenizer, row["state"], self.prompt) hit = self.state_data is not None and prefix == self.prefix if not prefix or ids[: len(prefix)] != prefix or len(ids) <= len(prefix): raise ValueError("State prefix does not match the full prompt") @@ -468,7 +471,7 @@ def score(self, row: dict) -> dict: result = _result( row, encoded, selected, vocabulary, self.metadata, "llamacpp-state-restore-v1", "quantized branch last-position logits over a restored prefix state", - ) + self.prompt) result.update( cache_hit=hit, prefix_tokens=len(prefix), @@ -508,7 +511,7 @@ def _chunks(prefix_length: int, suffix_lengths: list[int], budget: int, cap: int return chunks -def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): +def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096, prompt=None): """Prefill one exact state once, then score every criterion from branches. With a single sequence the branches are restored from a saved state, one at a time. With @@ -518,9 +521,10 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: raise ValueError("Shared scoring requires one nonempty exact state") if len({row["id"] for row in rows}) != len(rows): raise ValueError("Decision IDs must be unique") + prompt = resolve_prompt(prompt) started = time.perf_counter() - encoded = [model.encode_verified(row, max_tokens) for row in rows] - prefix = _state_prefix(tokenizer, rows[0]["state"]) + encoded = [model.encode_verified(row, max_tokens, prompt) for row in rows] + prefix = _state_prefix(tokenizer, rows[0]["state"], prompt) if not prefix or any(ids[: len(prefix)] != prefix or len(ids) <= len(prefix) for ids, _, _ in encoded): raise ValueError("The fixed state prefix does not match every full prompt") encode_seconds = time.perf_counter() - started @@ -546,7 +550,7 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: row, row_encoded, vocabulary[slots].tolist(), vocabulary, metadata, "llamacpp-state-restore-shared-v1", "quantized branch last-position logits over a restored prefix state", - )) + prompt)) branch_bytes = state_data[1] config = "llamacpp-state-restore-shared-v1" else: @@ -587,7 +591,7 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "llamacpp-seq-copy-parallel-v1", "quantized branch last-position logits over copied prefix sequences; " "one batched decode per chunk", - )) + prompt)) branch_bytes = 0 config = "llamacpp-seq-copy-parallel-v1" if engine.sequences == 1: diff --git a/src/semif_phase1/mlx_backend.py b/src/semif_phase1/mlx_backend.py index 1e0bf85..de7ff25 100644 --- a/src/semif_phase1/mlx_backend.py +++ b/src/semif_phase1/mlx_backend.py @@ -15,8 +15,8 @@ import re import time -from .core import softmax -from .direct import PROMPT_VERSION, encode_prompt +from .core import resolve_prompt, softmax +from .direct import encode_prompt from .shared import _state_prefix @@ -92,32 +92,33 @@ def load_model(source: str, revision: str, bits: int | None = None, *, return model, tokenizer, metadata -def _result(row, encoded, selected, metadata, mode): +def _result(row, encoded, selected, metadata, mode, prompt): ids, slots, prompt_hash = encoded return { "id": row["id"], "option_ids": [option["id"] for option in row["options"]], "probabilities": softmax(selected), "option_logits": selected, "answer_token_ids": slots, "input_tokens": len(ids), "input_ids_sha256": hashlib.sha256(json.dumps(ids).encode()).hexdigest(), - "prompt_sha256": prompt_hash, "prompt_version": PROMPT_VERSION, + "prompt_sha256": prompt_hash, "prompt_version": prompt.version, "model": {**metadata, "serving_config": f"mlx-{mode}-v1"}, "readout": "native last-position logits restricted to declared answer slots; no generated tokens", "probability_status": "conditional option score; uncalibrated as decision confidence", } -def score(model, tokenizer, row, metadata, max_tokens=4096): +def score(model, tokenizer, row, metadata, max_tokens=4096, prompt=None): import mlx.core as mx + prompt = resolve_prompt(prompt) mx.synchronize() started = time.perf_counter() - encoded = encode_prompt(tokenizer, row, max_tokens) + encoded = encode_prompt(tokenizer, row, max_tokens, prompt) ids, slots, _ = encoded mark = time.perf_counter() logits = model(mx.array([ids]))[0, -1].astype(mx.float32) selected = logits[mx.array(slots)].tolist() mx.synchronize() - result = _result(row, encoded, selected, metadata, "direct") + result = _result(row, encoded, selected, metadata, "direct", prompt) result.update(forward_seconds=time.perf_counter() - mark, total_seconds=time.perf_counter() - started) return result @@ -142,8 +143,9 @@ def _check_prefix(prefix, encoded): class SerialPrefixScorer: """Reuse only the current exact state; never mutate the retained prefix.""" - def __init__(self, model, tokenizer, metadata, max_tokens=4096): + def __init__(self, model, tokenizer, metadata, max_tokens=4096, prompt=None): self.model, self.tokenizer, self.metadata = model, tokenizer, metadata + self.prompt = resolve_prompt(prompt) self.max_tokens = max_tokens self.prefix = self.cache = None @@ -152,8 +154,8 @@ def score(self, row): mx.synchronize() started = time.perf_counter() - encoded = encode_prompt(self.tokenizer, row, self.max_tokens) - prefix = _state_prefix(self.tokenizer, row["state"]) + encoded = encode_prompt(self.tokenizer, row, self.max_tokens, self.prompt) + prefix = _state_prefix(self.tokenizer, row["state"], self.prompt) hit = self.cache is not None and prefix == self.prefix _check_prefix(prefix, [encoded]) prefill_seconds = 0.0 @@ -174,7 +176,7 @@ def score(self, row): selected = logits[mx.array(slots)].tolist() mx.synchronize() suffix_seconds = time.perf_counter() - mark - result = _result(row, encoded, selected, self.metadata, "serial") + result = _result(row, encoded, selected, self.metadata, "serial", self.prompt) result.update(cache_hit=hit, prefix_tokens=len(prefix), prefill_seconds=prefill_seconds, copy_seconds=copy_seconds, suffix_forward_seconds=suffix_seconds, forward_seconds=prefill_seconds + suffix_seconds, @@ -182,7 +184,7 @@ def score(self, row): return result -def score_shared(model, tokenizer, rows, metadata, max_tokens=4096): +def score_shared(model, tokenizer, rows, metadata, max_tokens=4096, prompt=None): """Prefill once, merge independent cache branches, then score padded suffixes. Right padding is masked by native recurrent caches. Causal attention prevents @@ -197,8 +199,9 @@ def score_shared(model, tokenizer, rows, metadata, max_tokens=4096): raise ValueError("Decision IDs must be unique") mx.synchronize() started = time.perf_counter() - encoded = [encode_prompt(tokenizer, row, max_tokens) for row in rows] - prefix = _state_prefix(tokenizer, rows[0]["state"]) + prompt = resolve_prompt(prompt) + encoded = [encode_prompt(tokenizer, row, max_tokens, prompt) for row in rows] + prefix = _state_prefix(tokenizer, rows[0]["state"], prompt) _check_prefix(prefix, encoded) suffixes = [ids[len(prefix):] for ids, _, _ in encoded] lengths = [len(ids) for ids in suffixes] @@ -225,7 +228,7 @@ def score_shared(model, tokenizer, rows, metadata, max_tokens=4096): mx.eval(selected) mx.synchronize() suffix_seconds = time.perf_counter() - mark - results = [_result(row, enc, values.tolist(), metadata, "shared") + results = [_result(row, enc, values.tolist(), metadata, "shared", prompt) for row, enc, values in zip(rows, encoded, selected)] timing = { "total_seconds": time.perf_counter() - started, "encode_seconds": encode_seconds, diff --git a/src/semif_phase1/serial.py b/src/semif_phase1/serial.py index 9beb8c7..a6bb670 100644 --- a/src/semif_phase1/serial.py +++ b/src/semif_phase1/serial.py @@ -8,26 +8,27 @@ import json import time -from .core import direct_messages, softmax, synchronize -from .direct import PROMPT_VERSION, encode_prompt +from .core import resolve_prompt, softmax, synchronize +from .direct import encode_prompt -def _state_prefix(tokenizer, state) -> list[int]: +def _state_prefix(tokenizer, state, prompt=None) -> list[int]: + prompt = resolve_prompt(prompt) row = { "id": "prefix-only", "state": state, "question": "prefix boundary placeholder", "options": [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"}], } - turns = direct_messages(row) - prompt = tokenizer.apply_chat_template( + turns = prompt.messages(row) + rendered = tokenizer.apply_chat_template( turns, tokenize=False, add_generation_prompt=True, enable_thinking=False ) payload = turns[-1]["content"] - evidence = json.dumps({"evidence": state}, ensure_ascii=False)[:-1] - if prompt.count(payload) != 1 or not payload.startswith(evidence): + evidence = prompt.evidence_text(state) + if rendered.count(payload) != 1 or not payload.startswith(evidence): raise ValueError("Cannot establish a deterministic evidence prefix") - text = prompt[: prompt.index(payload)] + evidence + text = rendered[: rendered.index(payload)] + evidence return tokenizer.encode(text, add_special_tokens=False)[:-1] @@ -43,9 +44,10 @@ def _cached_forward(model, inputs): class SerialPrefixScorer: """Cache the current state, then score independent copied suffix branches.""" - def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096): + def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096, prompt=None): self.model = model self.tokenizer = tokenizer + self.prompt = resolve_prompt(prompt) self.metadata = {**metadata, "serving_config": "native-state-prefix-cache-v1"} self.max_tokens = max_tokens self.device = next(model.parameters()).device @@ -57,9 +59,9 @@ def score(self, row: dict) -> dict: import torch started = time.perf_counter() - ids, slots, prompt_hash = encode_prompt(self.tokenizer, row, self.max_tokens) + ids, slots, prompt_hash = encode_prompt(self.tokenizer, row, self.max_tokens, self.prompt) hit = self.cache is not None and row["state"] == self.state - prefix = self.prefix if hit else _state_prefix(self.tokenizer, row["state"]) + prefix = self.prefix if hit else _state_prefix(self.tokenizer, row["state"], self.prompt) if not prefix or ids[: len(prefix)] != prefix or len(ids) <= len(prefix): raise ValueError("State prefix does not match the full prompt") sync = lambda: synchronize(self.device) @@ -110,7 +112,7 @@ def score(self, row: dict) -> dict: "answer_token_ids": slots, "input_tokens": len(ids), "prompt_sha256": prompt_hash, - "prompt_version": PROMPT_VERSION, + "prompt_version": self.prompt.version, "model": self.metadata, "readout": "native-state-prefix-cache-last-position", "probability_status": "conditional option score; uncalibrated as decision confidence", diff --git a/src/semif_phase1/shared.py b/src/semif_phase1/shared.py index ccb0c2a..b0a30ca 100644 --- a/src/semif_phase1/shared.py +++ b/src/semif_phase1/shared.py @@ -7,11 +7,12 @@ import json import time -from .core import direct_messages, softmax, synchronize -from .direct import PROMPT_VERSION, encode_prompt +from .core import resolve_prompt, softmax, synchronize +from .direct import encode_prompt -def _state_prefix(tokenizer, state) -> list[int]: +def _state_prefix(tokenizer, state, prompt=None) -> list[int]: + prompt = resolve_prompt(prompt) row = { "id": "prefix-only", "state": state, @@ -22,17 +23,17 @@ def _state_prefix(tokenizer, state) -> list[int]: {"id": "no", "description": "No"}, ], } - turns = direct_messages(row) - prompt = tokenizer.apply_chat_template( + turns = prompt.messages(row) + rendered = tokenizer.apply_chat_template( turns, tokenize=False, add_generation_prompt=True, enable_thinking=False ) payload = turns[-1]["content"] - if prompt.count(payload) != 1: + if rendered.count(payload) != 1: raise ValueError("Cannot locate the unmodified evidence payload in the chat template") - evidence = json.dumps({"evidence": state}, ensure_ascii=False)[:-1] + evidence = prompt.evidence_text(state) if not payload.startswith(evidence): raise ValueError("Evidence serialization changed") - text = prompt[: prompt.index(payload)] + evidence + text = rendered[: rendered.index(payload)] + evidence # Appending JSON punctuation can merge with the final boundary token. return tokenizer.encode(text, add_special_tokens=False)[:-1] @@ -51,17 +52,18 @@ def _suffix_layout(sequences: list[list[int]], prefix_length: int, pad_id: int): return {"input_ids": ids, "attention_mask": masks, "position_ids": positions}, ends -def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): +def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096, prompt=None): """Return all option distributions together after one state prefill.""" import torch + prompt = resolve_prompt(prompt) if not rows or any(row["state"] != rows[0]["state"] for row in rows[1:]): raise ValueError("Shared scoring requires one nonempty exact state") if len({row["id"] for row in rows}) != len(rows): raise ValueError("Decision IDs must be unique") started = time.perf_counter() - encoded = [encode_prompt(tokenizer, row, max_tokens) for row in rows] - prefix = _state_prefix(tokenizer, rows[0]["state"]) + encoded = [encode_prompt(tokenizer, row, max_tokens, prompt) for row in rows] + prefix = _state_prefix(tokenizer, rows[0]["state"], prompt) if not prefix or any(ids[: len(prefix)] != prefix or len(ids) <= len(prefix) for ids, _, _ in encoded): raise ValueError("The fixed state prefix does not match every full prompt") pad = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id @@ -154,7 +156,7 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "option_logits": selected, "input_tokens": len(ids), "prompt_sha256": prompt_hash, - "prompt_version": PROMPT_VERSION, + "prompt_version": prompt.version, "model": {**metadata, "serving_config": ( "native-state-prefix-looped-v1" if looped else "native-state-prefix-parallel-v1" )}, diff --git a/tests/test_cli.py b/tests/test_cli.py index 2a1f484..8663058 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -9,6 +9,7 @@ import semif_phase1 from semif_phase1 import cli +from semif_phase1.core import DEFAULT_PROMPT from semif_phase1.cli import main @@ -34,7 +35,7 @@ def test_cli_passes_cache_limit_to_loader(tmp_path, monkeypatch, limit): DEFAULT_CACHE_LIMIT_MIB=256, load_model=lambda source, revision, bits, *, cache_limit_mib: (None, None, {'limit': cache_limit_mib}), - score=lambda model, tokenizer, row, metadata, max_tokens: metadata, + score=lambda model, tokenizer, row, metadata, max_tokens, prompt=None: metadata, SerialPrefixScorer=None, score_shared=None, ) monkeypatch.setattr(semif_phase1, 'mlx_backend', fake_backend, raising=False) @@ -111,11 +112,11 @@ def test_mlx_routes_to_matching_scorer(run_cli, backends, mode): mlx.load_model.assert_called_once() assert all(mock.call_count == 0 for mock in vars(backends.torch).values()) if mode == "direct": - assert mlx.score.call_args_list == [call(model, tokenizer, row, metadata, 128) for row in run_cli.rows] + assert mlx.score.call_args_list == [call(model, tokenizer, row, metadata, 128, prompt=DEFAULT_PROMPT) for row in run_cli.rows] elif mode == "serial": assert mlx.SerialPrefixScorer.return_value.score.call_args_list == [call(row) for row in run_cli.rows] else: - mlx.score_shared.assert_called_once_with(model, tokenizer, run_cli.rows, metadata, 128) + mlx.score_shared.assert_called_once_with(model, tokenizer, run_cli.rows, metadata, 128, prompt=DEFAULT_PROMPT) expected = backends.results if mode != "shared" else [ {**result, "shared_timing": backends.timing} for result in backends.results ] diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index c98314c..6d19fb5 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -38,7 +38,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences: (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens, "gpu_layers": gpu_layers, "sequences": sequences}), - score=lambda model, tokenizer, row, metadata, max_tokens: metadata, + score=lambda model, tokenizer, row, metadata, max_tokens, prompt=None: metadata, SerialPrefixScorer=None, score_shared=None, ) monkeypatch.setattr(semif_phase1, "llamacpp_backend", fake_backend, raising=False) @@ -154,14 +154,14 @@ class FakeModel: def __init__(self): self.engine = FakeEngine() - def encode_verified(self, row, max_tokens): + def encode_verified(self, row, max_tokens, prompt=None): prefix = [1 if key == "a" else 2 for key in row["state"]] return prefix + [9], [0, 1], "hash" monkeypatch.setattr( llamacpp_backend, "_state_prefix", - lambda tokenizer, state: [1 if key == "a" else 2 for key in state], + lambda tokenizer, state, prompt=None: [1 if key == "a" else 2 for key in state], ) model = FakeModel() scorer = llamacpp_backend.SerialPrefixScorer(model, object(), {}) diff --git a/tests/test_prompt.py b/tests/test_prompt.py new file mode 100644 index 0000000..43e391a --- /dev/null +++ b/tests/test_prompt.py @@ -0,0 +1,90 @@ +"""The pluggable prompt: the default renders the published words, other wordings keep the shape.""" + +import json +import subprocess +import sys + +import pytest + +from semif_phase1.core import DEFAULT_PROMPT, DIRECT_SYSTEM, PROMPTS, Prompt, direct_messages, resolve_prompt +from semif_phase1.direct import PROMPT_VERSION +from semif_phase1.serial import _state_prefix +from test_serial import Tokenizer + +ROW = { + "id": "x", + "state": "owned evidence", + "question": "Which answer follows?", + "options": [{"id": "yes", "description": "Yes."}, {"id": "no", "description": "No."}], +} + + +def test_default_prompt_is_the_published_wording_byte_for_byte(): + payload = {"evidence": "owned evidence", "criterion": "Which answer follows?", + "options": [{"letter": "A", "description": "Yes."}, {"letter": "B", "description": "No."}]} + assert direct_messages(ROW) == [ + {"role": "system", "content": DIRECT_SYSTEM}, + {"role": "user", "content": json.dumps(payload, ensure_ascii=False)}, + ] + assert PROMPT_VERSION == DEFAULT_PROMPT.version == "direct-options-v1" + assert resolve_prompt(None) is resolve_prompt("en") is DEFAULT_PROMPT + + +def test_french_prompt_keeps_the_shape_and_changes_the_words(): + fr = resolve_prompt("fr") + messages = fr.messages(ROW) + payload = json.loads(messages[1]["content"]) + assert list(payload) == ["elements", "critere", "options"] + assert payload["elements"] == "owned evidence" + assert payload["options"][0] == {"lettre": "A", "description": "Yes."} + assert messages[0]["content"].startswith("Applique") + assert fr.version != DEFAULT_PROMPT.version + assert len({prompt.version for prompt in PROMPTS.values()}) == len(PROMPTS) + + +def test_state_prefix_follows_the_prompt_and_still_stops_before_the_question(): + for name in PROMPTS: + prompt = PROMPTS[name] + prefix = bytes(_state_prefix(Tokenizer(), "owned state", prompt)).decode() + assert prefix.startswith("HEADER\n{\"" + prompt.evidence_key + "\": \"owned state") + assert "prefix boundary placeholder" not in prefix + assert prompt.options_key not in prefix + assert _state_prefix(Tokenizer(), "owned state") == _state_prefix(Tokenizer(), "owned state", "en") + + +def test_prompt_file_round_trip(tmp_path): + path = tmp_path / "prompt.json" + path.write_text(json.dumps({"version": "direct-options-de-test", "system": "Wende das Kriterium an.", + "keys": {"evidence": "beleg", "letter": "buchstabe"}})) + prompt = resolve_prompt(str(path)) + assert prompt == Prompt("direct-options-de-test", "Wende das Kriterium an.", evidence_key="beleg", letter_key="buchstabe") + assert prompt.evidence_text({"a": 1}) == '{"beleg": {"a": 1}' + + +@pytest.mark.parametrize("data,message", [ + ({"system": "no version"}, "needs 'version' and 'system'"), + ({"version": "v", "system": "s", "keys": {"state": "x"}}, "may only rename"), + ({"version": "v", "system": "s", "keys": {"evidence": "same", "criterion": "same"}}, "distinct"), + ({"version": "direct-options-v1", "system": "other words"}, "belongs to a built-in prompt"), + ({"version": " ", "system": "s"}, "nonempty version"), +]) +def test_invalid_prompt_files_are_rejected(tmp_path, data, message): + path = tmp_path / "prompt.json" + path.write_text(json.dumps(data)) + with pytest.raises(ValueError, match=message): + resolve_prompt(str(path)) + + +def test_unknown_prompt_name_is_rejected(): + with pytest.raises(ValueError, match="Unknown prompt"): + resolve_prompt("klingon") + + +def test_scoring_modules_import_without_torch(): + """The llama.cpp path needs only transformers for its tokenizer; torch stays a lazy import.""" + code = ("import sys; sys.modules['torch'] = None; " + "import semif_phase1.cli, semif_phase1.core, semif_phase1.direct, semif_phase1.serial, " + "semif_phase1.shared, semif_phase1.llamacpp_backend; print('ok')") + completed = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True) + assert completed.returncode == 0, completed.stderr + assert completed.stdout.strip() == "ok" diff --git a/tests/test_shared.py b/tests/test_shared.py index a91875d..3c3b4a2 100644 --- a/tests/test_shared.py +++ b/tests/test_shared.py @@ -76,8 +76,8 @@ def test_score_shared_looped_path_forwards_one_row_per_call(monkeypatch): for index, key in enumerate(("a", "b")) ] monkeypatch.setattr("semif_phase1.shared.encode_prompt", - lambda tok, row, limit: ([1] * 8, [2, 3], f"hash-{row['id']}")) - monkeypatch.setattr("semif_phase1.shared._state_prefix", lambda tokenizer, state: [1] * 6) + lambda tok, row, limit, prompt=None: ([1] * 8, [2, 3], f"hash-{row['id']}")) + monkeypatch.setattr("semif_phase1.shared._state_prefix", lambda tokenizer, state, prompt=None: [1] * 6) calls = [] results, timing = score_shared(FakeModel(calls, torch.device("mps")), SimpleNamespace(pad_token_id=0, eos_token_id=1), rows_data, {}, 4096)